@article{dever_wiedbusch_park_llinas_lester_azevedo_2024, title={Assessing the Complexity of Gaming Mechanics During Science Learning}, volume={14475}, ISBN={["978-3-031-49064-4"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-49065-1_29}, abstractNote={Game-based learning environments (GBLEs) incorporate game mechanics, i.e., learning and assessment mechanics, to increase domain knowledge while maintaining learner engagement. Although GBLEs have been developed to improve science learning, learners have attained lower science achievement scores over the past decade as they progress through school. As such, there is a need to better understand how learners use game mechanics as they learn about science content. This study aimed to understand how learners generally use and transition between learning and assessment mechanics while learning about science with a GBLE and how those transitions were related to learning outcomes (i.e., learning gains, game success). High-school students (N = 137) were recruited to play Crystal Island, a GBLE about microbiology. Results found that participants used static learning mechanics (e.g., virtual books about microbiology) most often, followed by game and content assessment mechanics, and lastly followed by aid and dynamic learning mechanics. Further results found that several sequential transition probabilities were related to lower learning outcomes with a few transitions positively relating to game completion success. Findings from this study also show that the type of game mechanic, as well as the direction of transitions across game mechanics significantly relate to learning outcomes. These findings provide insights into how to develop scaffolding techniques for improving science learning outcomes.}, journal={GAMES AND LEARNING ALLIANCE, GALA 2023}, author={Dever, Daryn A. and Wiedbusch, Megan and Park, Saerok and Llinas, Andrea and Lester, James and Azevedo, Roger}, year={2024}, pages={299–308} }
 @article{pugatch_blum_barbaresi_rowe_berna_hennigan_giovanelli_penilla_tebb_mott_et al._2024, title={Engagement of adolescents with ADHD in a narrative-centered game-based behavior change environment to reduce alcohol use}, volume={8}, ISSN={["2504-284X"]}, DOI={10.3389/feduc.2023.1183994}, abstractNote={BackgroundAttention deficit hyperactivity disorder (ADHD) affects about 13% of adolescents and is associated with substance use-related morbidity and mortality. While evidence on effective interventions to reduce alcohol use among adolescents with ADHD is limited, parent-teen communication about alcohol use has been found to be protective. Other approaches, such as educational interventions, hold promise to reduce alcohol-related harms in adolescents with ADHD. Digital technology offers an innovative approach to health behavior change, expanding access to services and may promote learning for neurodiverse youth, including teens with ADHD. INSPIRE, a narrative-centered game-based behavior change environment designed to promote self-regulation and self-efficacy to prevent risky alcohol use has been found to engage a general adolescent population. The goals of this pilot study are (1) to examine the engagement of youth with ADHD in INSPIRE; and (2) to examine if INSPIRE fosters parent-teen communication.}, journal={FRONTIERS IN EDUCATION}, author={Pugatch, Marianne and Blum, Nathan J. and Barbaresi, William J. and Rowe, Jonathan and Berna, Mark and Hennigan, Sean and Giovanelli, Alison and Penilla, Carlos and Tebb, Kathleen P. and Mott, Megan and et al.}, year={2024}, month={Jan} }
 @article{lester_bansal_biswas_hmelo-silver_roschelle_rowe_2024, title={The AI Institute for Engaged Learning}, ISSN={["2371-9621"]}, DOI={10.1002/aaai.12161}, abstractNote={Abstract}, journal={AI MAGAZINE}, author={Lester, James and Bansal, Mohit and Biswas, Gautam and Hmelo-Silver, Cindy and Roschelle, Jeremy and Rowe, Jonathan}, year={2024}, month={Feb} }
 @article{wiedbusch_lester_azevedo_2023, title={A multi-level growth modeling approach to measuring learner attention with metacognitive pedagogical agents}, ISSN={["1556-1631"]}, DOI={10.1007/s11409-023-09336-z}, journal={METACOGNITION AND LEARNING}, author={Wiedbusch, Megan and Lester, James and Azevedo, Roger}, year={2023}, month={Mar} }
 @article{bae_feng_glazewski_hmelo-silver_chen_mott_lee_lester_2023, title={Co-designing a Classroom Orchestration Assistant for Game-based PBL Environments}, ISSN={["1559-7075"]}, DOI={10.1007/s11528-023-00903-4}, journal={TECHTRENDS}, author={Bae, Haesol and Feng, Chen and Glazewski, Krista and Hmelo-Silver, Cindy E. and Chen, Yuxin and Mott, Bradford W. and Lee, Seung Y. and Lester, James C.}, year={2023}, month={Nov} }
 @article{emerson_min_azevedo_lester_2023, title={Early prediction of student knowledge in game-based learning with distributed representations of assessment questions}, volume={54}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.13281}, abstractNote={Abstract}, number={1}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Emerson, Andrew and Min, Wookhee and Azevedo, Roger and Lester, James}, year={2023}, month={Jan}, pages={40–57} }
 @article{mott_gupta_glazewskianne_ottenbreit-leftwich_hmelo-silver_scribner_lee_lester_2023, title={Fostering Upper Elementary AI Education: Iteratively Refining a Use-Modify-Create Scaffolding Progression for AI Planning}, DOI={10.1145/3587103.3594170}, abstractNote={The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.}, journal={PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL. 2}, author={Mott, Bradford and Gupta, Anisha and GlazewskiAnne, Krista and Ottenbreit-Leftwich, Anne and Hmelo-Silver, Cindy and Scribner, Adam and Lee, Seung and Lester, James}, year={2023}, pages={647–647} }
 @article{wang_lester_2023, title={K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-023-00358-x}, abstractNote={Abstract}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Wang, Ning and Lester, James}, year={2023}, month={Jun} }
 @article{wang_lester_2023, title={Preface to the Special Issue on K-12 AI Education}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-023-00363-0}, abstractNote={It is widely recognized that AI is beginning to profoundly impact society around the globe. These developments are introducing new opportunities, presenting new risks, and fundamentally reshaping the current and future workforce. As such, we must now answer critically important questions: How can we prepare K-12 students for an AI-permeated future? How do K-12 students conceive of AI and what do they need to know to be effective consumers of AI technologies? What competencies do K-12 students need to acquire to be prepared for workplaces where human-AI teaming is the norm? What do future knowledge workers, including but not limited to those in STEM, need to learn in primary and secondary school to set the stage for their careers, which will no doubt require the ability to effectively interact with AI tools? How can K-12 education best prepare future AI developers, engineers, and researchers? This special issue explores the emerging field of K-12 AI education research.}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Wang, Ning and Lester, James}, year={2023}, month={Aug} }
 @article{giovanelli_rowe_taylor_berna_tebb_penilla_pugatch_lester_ozer_2023, title={Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions}, volume={25}, ISSN={["1438-8871"]}, DOI={10.2196/40306}, abstractNote={Understanding and optimizing adolescent-specific engagement with behavior change interventions will open doors for providers to promote healthy changes in an age group that is simultaneously difficult to engage and especially important to affect. For digital interventions, there is untapped potential in combining the vastness of process-level data with the analytical power of artificial intelligence (AI) to understand not only how adolescents engage but also how to improve upon interventions with the goal of increasing engagement and, ultimately, efficacy. Rooted in the example of the INSPIRE narrative-centered digital health behavior change intervention (DHBCI) for adolescent risky behaviors around alcohol use, we propose a framework for harnessing AI to accomplish 4 goals that are pertinent to health care providers and software developers alike: measurement of adolescent engagement, modeling of adolescent engagement, optimization of current interventions, and generation of novel interventions. Operationalization of this framework with youths must be situated in the ethical use of this technology, and we have outlined the potential pitfalls of AI with particular attention to privacy concerns for adolescents. Given how recently AI advances have opened up these possibilities in this field, the opportunities for further investigation are plenty.}, journal={JOURNAL OF MEDICAL INTERNET RESEARCH}, author={Giovanelli, Alison and Rowe, Jonathan and Taylor, Madelynn and Berna, Mark and Tebb, Kathleen P. and Penilla, Carlos and Pugatch, Marianne and Lester, James and Ozer, Elizabeth M.}, year={2023}, month={May} }
 @article{saleh_phillips_hmelo-silver_glazewski_mott_lester_2022, title={A learning analytics approach towards understanding collaborative inquiry in a problem-based learning environment}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.13198}, abstractNote={Abstract}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Saleh, Asmalina and Phillips, Tanner M. and Hmelo-Silver, Cindy E. and Glazewski, Krista D. and Mott, Bradford W. and Lester, James C.}, year={2022}, month={Feb} }
 @article{cloude_dever_hahs-vaughn_emerson_azevedo_lester_2022, title={Affective Dynamics and Cognition During Game-Based Learning}, volume={13}, ISSN={["1949-3045"]}, DOI={10.1109/TAFFC.2022.3210755}, abstractNote={Inability to regulate affective states can impact one's capacity to engage in higher-order thinking like scientific reasoning with game-based learning environments. Many efforts have been made to build affect-aware systems to mitigate the potentially detrimental effects of negative affect. Yet, gaps in research exist since accurately capturing and modeling affect as a state that changes dynamically over time is methodologically and analytically challenging. In this paper, we calculated multilevel mixed effects growth models to assess whether seventy-eight participants’ (n = 78) time engaging in scientific reasoning (via logfiles and eye gaze) were related to time facially expressing confused, frustrated, and neutral states (via facial recognition software) during game-based learning with Crystal Island. The fitted model estimated significant positive relations between the time learners facially expressed confusion, frustration, and neutral states and time engaging in scientific-reasoning actions. The time individual learners facially expressed frustrated, confused, and neutral states explained a significant amount of variation in time engaging in scientific reasoning. Our finding emphasize that individual differences and agency may play a important role on relations between affective states, their dynamics, and higher-order cognition during game-based learning. Designing affect-aware game-based learning environments that track the dynamics within individual learners’ affective states may best support cognition.}, number={4}, journal={IEEE TRANSACTIONS ON AFFECTIVE COMPUTING}, author={Cloude, Elizabeth B. and Dever, Daryn A. and Hahs-Vaughn, Debbie L. and Emerson, Andrew J. and Azevedo, Roger and Lester, James}, year={2022}, month={Oct}, pages={1705–1717} }
 @article{horwitz_reichsman_lord_dorsey_wiebe_lester_2022, title={If We Build It, Will They Learn? An Analysis of Students' Understanding in an Interactive Game During and After a Research Project}, volume={8}, ISSN={["2211-1670"]}, DOI={10.1007/s10758-022-09617-7}, abstractNote={Studies of educational games often treat them as “black boxes” (Black and Wiliam in Phi Delta Kappan 80: 139–48, 1998; Buckley et al. in Int J LearnTechnol 5:166–190, 2010; Buckley et al. in J Sci Educ Technol 13: 23–41, 2010) and measure their effectiveness by exposing a treatment group of students to the game and comparing their performance on an external assessment to that of a control group taught the same material by some other method. This precludes the possibility of monitoring, evaluating, and reacting to the actions of individual students as they progress through the game. To do that, however, one must know what to look for because superficial measures of success are unlikely to identify unproductive behaviors such as “gaming the system.” (Baker in Philipp Comput J, 2011; Downs et al. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, USA, 2010) The research reported here advances the ultimate goal of creating educational games that can provide real time, meaningful feedback on the progress of their users, enabling teachers or the game itself to intervene in a timely manner. We present the results of an in-depth analysis of students’ actions in Geniventure, an interactive digital game designed to teach genetics to middle and high school students. Geniventure offers a sequence of challenges of increasing difficulty and records students’ actions as they progress. We analyzed the resulting log files, taking into account not only whether a student achieved a certain goal, but also the quality of the student’s performance on each attempt. Using this information, we quantified students’ performance and correlated it to their learning gain as estimated by scores on identical multiple-choice tests administered before and after exposure to Geniventure. This analysis was performed in classes taught by teachers who had participated in professional development as part of a research project. A two-tailed paired-sample t-test of mean pre-test and post-test scores in these classes indicates a significant positive difference with a large effect size. Multivariate regression analysis of log data finds no correlation between students’ post-test scores and their performance on “practice” challenges that invite experimentation, but a highly significant positive correlation with performance on “assessment” challenges, presented immediately following the practice challenges, that required students to invoke relevant mental models. We repeated this analysis with similar results using a second group of classes led by teachers who implemented Geniventure on their own after the conclusion of, and with no support from, the research project.}, journal={TECHNOLOGY KNOWLEDGE AND LEARNING}, author={Horwitz, Paul and Reichsman, Frieda and Lord, Trudi and Dorsey, Chad and Wiebe, Eric and Lester, James}, year={2022}, month={Aug} }
 @article{zhang_hutt_ocumpaugh_henderson_goslen_rowe_boyer_wiebe_mott_lester_2022, title={Investigating Student Interest and Engagement in Game-Based Learning Environments}, volume={13355}, ISBN={["978-3-031-11643-8"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11644-5_72}, abstractNote={As a cognitive and affective state, interest promotes engagement, facilitates self-regulated learning, and is positively associated with learning outcomes. Research has shown that interest interacts with prior knowledge, but few studies have investigated these issues in the context of adaptive game-based learning environments. Using three subscales from the User Engagement Scale, we examine data from middle school students (N = 77) who interacted with Crystal Island in their regular science class to explore the relationship between interest, knowledge, and learning. We found that interest is significantly related to performance (both knowledge assessment and game completion), suggesting that students with high interest are likely to perform better academically, but also be more engaged in the in-game objectives. These findings have implications both for designers who seek to identify students with lower interest and for those who hope to create adaptive supports.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I}, author={Zhang, Jiayi and Hutt, Stephen and Ocumpaugh, Jaclyn and Henderson, Nathan and Goslen, Alex and Rowe, Jonathan P. and Boyer, Kristy Elizabeth and Wiebe, Eric and Mott, Bradford and Lester, James}, year={2022}, pages={711–716} }
 @article{park_mott_lee_gupta_jantaraweragul_glazewski_scribner_ottenbreit-leftwich_hmelo-silver_lester_2022, title={Investigating a visual interface for elementary students to formulate AI planning tasks}, volume={73}, ISSN={["2665-9182"]}, DOI={10.1016/j.cola.2022.101157}, abstractNote={Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this article, we propose a visual interface to enable upper elementary students (grades 3-5, ages 8-11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.}, journal={JOURNAL OF COMPUTER LANGUAGES}, author={Park, Kyungjin and Mott, Bradford and Lee, Seung and Gupta, Anisha and Jantaraweragul, Katie and Glazewski, Krista and Scribner, J. Adam and Ottenbreit-Leftwich, Anne and Hmelo-Silver, Cindy E. and Lester, James}, year={2022}, month={Dec} }
 @article{ottenbreit-leftwich_glazewski_jeon_jantaraweragul_hmelo-silver_scribner_lee_mott_lester_2022, title={Lessons Learned for AI Education with Elementary Students and Teachers}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-022-00304-3}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Ottenbreit-Leftwich, Anne and Glazewski, Krista and Jeon, Minji and Jantaraweragul, Katie and Hmelo-Silver, Cindy E. and Scribner, Adam and Lee, Seung and Mott, Bradford and Lester, James}, year={2022}, month={Sep} }
 @article{goslen_carpenter_rowe_henderson_azevedo_lester_2022, title={Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning}, volume={13355}, ISBN={["978-3-031-11643-8"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11644-5_7}, abstractNote={Goal setting and planning are integral components of self-regulated learning. Many students struggle to set meaningful goals and build relevant plans. Adaptive learning environments show significant potential for scaffolding students’ goal setting and planning processes. An important requirement for such scaffolding is the ability to perform student plan recognition, which involves recognizing students’ goals and plans based upon the observations of their problem-solving actions. We introduce a novel plan recognition framework that leverages trace log data from student interactions within a game-based learning environment called CRYSTAL ISLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. We formalize student plan recognition in terms of two complementary tasks: (1) classifying students’ selected problem-solving goals, and (2) classifying the sequences of actions that students indicate will achieve their goals. Utilizing trace log data from 144 middle school students’ interactions with CRYSTAL ISLAND, we evaluate a range of machine learning models for student goal and plan recognition. All machine learning-based techniques outperform the majority baseline, with LSTMs outperforming other models for goal recognition and naive Bayes performing best for plan recognition. Results show the potential for automatically recognizing students’ problem-solving goals and plans in game-based learning environments, which has implications for providing adaptive support for student self-regulated learning.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I}, author={Goslen, Alex and Carpenter, Dan and Rowe, Jonathan P. and Henderson, Nathan and Azevedo, Roger and Lester, James}, year={2022}, pages={78–89} }
 @article{fahid_acosta_lee_carpenter_mott_bae_saleh_brush_glazewski_hmelo-silver_et al._2022, title={Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_38}, abstractNote={Collaborative game-based learning environments offer significant promise for creating effective and engaging group learning experiences. These environments enable small groups of students to work together toward a common goal by sharing information, asking questions, and constructing explanations. However, students periodically disengage from the learning process, which negatively affects their learning, and the impacts are more severe in collaborative learning environments as disengagement can propagate, affecting participation across the group. Here, we introduce a multimodal behavioral disengagement detection framework that uses facial expression analysis in conjunction with natural language analyses of group chat. We evaluate the framework with students interacting with a collaborative game-based learning environment for middle school science education. The multimodal behavioral disengagement detection framework integrating both facial expression and group chat modalities achieves higher levels of predictive accuracy than those of baseline unimodal models.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Acosta, Halim and Lee, Seung and Carpenter, Dan and Mott, Bradford and Bae, Haesol and Saleh, Asmalina and Brush, Thomas and Glazewski, Krista and Hmelo-Silver, Cindy E. and et al.}, year={2022}, pages={218–221} }
 @article{fahid_rowe_spain_goldberg_pokorny_lester_2022, title={Robust Adaptive Scaffolding with Inverse Reinforcement Learning-Based Reward Design}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_35}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Rowe, Jonathan P. and Spain, Randall D. and Goldberg, Benjamin S. and Pokorny, Robert and Lester, James}, year={2022}, pages={204–207} }
 @article{spain_rowe_smith_goldberg_pokorny_mott_lester_2021, title={A reinforcement learning approach to adaptive remediation in online training}, volume={7}, ISSN={["1557-380X"]}, DOI={10.1177/15485129211028317}, abstractNote={ Advances in artificial intelligence (AI) and machine learning can be leveraged to tailor training based on the goals, learning needs, and preferences of learners. A key component of adaptive training systems is tutorial planning, which controls how scaffolding is structured and delivered to learners to create dynamically personalized learning experiences. The goal of this study was to induce data-driven policies for tutorial planning using reinforcement learning (RL) to provide adaptive scaffolding based on the Interactive, Constructive, Active, Passive framework for cognitive engagement. We describe a dataset that was collected to induce RL-based scaffolding policies, and we present the results of our policy analyses. Results showed that the best performing policies optimized learning gains by inducing an adaptive fading approach in which learners received less cognitively engaging forms of remediation as they advanced through the training course. This policy was consistent with preliminary analyses that showed constructive remediation became less effective as learners progressed through the training session. Results also showed that learners’ prior knowledge impacted the type of scaffold that was recommended, thus showing evidence of an aptitude–treatment interaction. We conclude with a discussion of how AI-based training can be leveraged to enhance training effectiveness as well as directions for future research. }, journal={JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS}, author={Spain, Randall and Rowe, Jonathan and Smith, Andy and Goldberg, Benjamin and Pokorny, Robert and Mott, Bradford and Lester, James}, year={2021}, month={Jul} }
 @article{fahid_rowe_spain_goldberg_pokorny_lester_2021, title={Adaptively Scaffolding Cognitive Engagement with Batch Constrained Deep Q-Networks}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78292-4_10}, abstractNote={Scaffolding student engagement is a central challenge in adaptive learning environments. The ICAP framework defines levels of cognitive engagement with a learning activity in terms of four different engagement modes—Interactive, Constructive, Active, and Passive—and it predicts that increased cognitive engagement will yield improved learning. However, a key open question is how best to translate the ICAP theory into the design of adaptive scaffolding in adaptive learning environments. Specifically, should scaffolds be designed to require the highest levels of cognitive engagement (i.e., Interactive and Constructive modes) with every instance of feedback or knowledge component? To answer this question, in this paper we investigate a data-driven pedagogical modeling framework based on batch-constrained deep Q-networks, a type of deep reinforcement learning (RL) method, to induce policies for delivering ICAP-inspired scaffolding in adaptive learning environments. The policies are trained with log data from 487 learners as they interacted with an adaptive learning environment that provided ICAP-inspired feedback and remediation. Results suggest that adaptive scaffolding policies induced with batch-constrained deep Q-networks outperform heuristic policies that strictly follow the ICAP model without RL-based tailoring. The findings demonstrate the utility of deep RL for tailoring scaffolding for learner cognitive engagement.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Fahid, Fahmid Morshed and Rowe, Jonathan P. and Spain, Randall D. and Goldberg, Benjamin S. and Pokorny, Robert and Lester, James}, year={2021}, pages={113–124} }
 @article{park_mott_lee_glazewski_scribner_ottenbreit-leftwich_hmelo-silver_lester_2021, title={Designing a Visual Interface for Elementary Students to Formulate AI Planning Tasks}, ISSN={["1943-6092"]}, DOI={10.1109/VL/HCC51201.2021.9576163}, abstractNote={Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this paper, we propose a visual interface to enable upper elementary students (grades 3–5, ages 8–11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a qualitative study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.}, journal={2021 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC 2021)}, author={Park, Kyungjin and Mott, Bradford and Lee, Seung and Glazewski, Krista and Scribner, J. Adam and Ottenbreit-Leftwich, Anne and Hmelo-Silver, Cindy E. and Lester, James}, year={2021} }
 @article{park_sohn_mott_min_saleh_glazewski_hmelo-silver_lester_2021, title={Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments}, DOI={10.1145/3448139.3448178}, abstractNote={Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students’ in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students’ learning outcomes and varies across gender and students’ prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.}, journal={LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE}, author={Park, Kyungjin and Sohn, Hyunwoo and Mott, Bradford W. and Min, Wookhee and Saleh, Asmalina and Glazewski, Krista D. and Hmelo-Silver, Cindy E. and Lester, James C.}, year={2021}, pages={405–415} }
 @article{dever_wiedbusch_cloude_lester_azevedo_2021, title={Emotions and the Comprehension of Single versus Multiple Texts during Game-based Learning}, ISSN={["1532-6950"]}, DOI={10.1080/0163853X.2021.1950450}, abstractNote={ABSTRACT This study examined 57 learners’ emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners’ prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text comprehension. Analyses found that both discrete and non-discrete emotions were expressed during reading and answering in-game assessments of single-text comprehension. Learners expressed greater joy during reading and greater expressions of anger, confusion, and frustration during in-game assessments. Further results found that learners who expressed a high number of different emotions throughout reading and completing in-game assessments tended to have lower in-game comprehension scores whereas a higher number of different expressed emotions while completing in-game assessments was associated with greater posttest comprehension. Finally, while increased prior knowledge was associated with higher single- and multiple-text comprehension, there was no interaction between prior knowledge and emotions on multiple-text comprehension. Overall, this study found that (1) learners often express more than one emotion during GBLE activities, (2) emotions expressed while learning with a GBLE shift across different activities, and (3) emotions are related to demonstrated comprehension, but the type of activity influences this relationship. Results from this study provide implications for how emotions can be examined as learners engage in GBLE activities as well as the design of GBLEs to support learners’ emotions accounting for different activity demands to increase comprehension of single and multiple texts.}, journal={DISCOURSE PROCESSES}, author={Dever, Daryn A. and Wiedbusch, Megan D. and Cloude, Elizabeth B. and Lester, James and Azevedo, Roger}, year={2021}, month={Aug} }
 @article{henderson_min_rowe_lester_2021, title={Enhancing Multimodal Affect Recognition with Multi-Task Affective Dynamics Modeling}, ISSN={["2156-8103"]}, DOI={10.1109/ACII52823.2021.9597432}, abstractNote={Accurately recognizing students’ affective states is critical for enabling adaptive learning environments to promote engagement and enhance learning outcomes. Multimodal approaches to student affect recognition capture multi-dimensional patterns of student behavior through the use of multiple data channels. An important factor in multimodal affect recognition is the context in which affect is experienced and exhibited. In this paper, we present a multimodal, multitask affect recognition framework that predicts students’ future affective states as auxiliary training tasks and uses prior affective states as input features to capture bi-directional affective dynamics and enhance the training of affect recognition models. Additionally, we investigate cross-stitch networks to maintain parameterized separation between shared and task-specific representations and task-specific uncertainty-weighted loss functions for contextual modeling of student affective states. We evaluate our approach using interaction and posture data captured from students engaged with a game-based learning environment for emergency medical training. Results indicate that the affective dynamics-based approach yields significant improvements in multimodal affect recognition across four different affective states.}, journal={2021 9TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)}, author={Henderson, Nathan and Min, Wookhee and Rowe, Jonathan and Lester, James}, year={2021} }
 @article{cloude_carpenter_dever_azevedo_lester_2021, title={Game-Based Learning Analytics for Supporting Adolescents' Reflection}, volume={8}, ISSN={["1929-7750"]}, DOI={10.18608/jla.2021.7371}, abstractNote={Reflection is critical for adolescents’ problem solving and learning in game-based learning environments (GBLEs). Yet challenges exist in the literature because most studies lack a theoretical perspective and clear operational definition to inform how and when reflection should be scaffolded during game-based learning. In this paper, we address these issues by studying the quantity and quality of 120 adolescents’ written reflections and their relation to their learning and problem solving with Crystal Island, a GBLE. Specifically, we (1) define reflection and how it relates to skill and knowledge acquisition; (2) review studies examining reflection and its relation to problem solving and learning with emerging technologies; and (3) provide direction for building reflection prompts into GBLEs that are aligned with the learning goals built into the learning session (e.g., learn about microbiology versus successfully solve a problem) to maximize adolescents’ reflection, learning, and performance. Overall, our findings emphasize how important it is to examine not only the quantity of reflection but also the depth of written reflection as it relates to specific learning goals. We discuss the implications of using game-learning analytics to guide instructional decision making in the classroom.}, number={2}, journal={JOURNAL OF LEARNING ANALYTICS}, author={Cloude, Elizabeth B. and Carpenter, Dan and Dever, Daryn A. and Azevedo, Roger and Lester, James}, year={2021}, pages={51–72} }
 @article{carpenter_cloude_rowe_azevedo_lester_2021, title={Investigating Student Reflection during Game-Based Learning in Middle Grades Science}, DOI={10.1145/3448139.3448166}, abstractNote={Reflection plays a critical role in learning by encouraging students to contemplate their knowledge and previous learning experiences to inform their future actions and higher-order thinking, such as reasoning and problem solving. Reflection is particularly important in inquiry-driven learning scenarios where students have the freedom to set goals and regulate their own learning. However, despite the importance of reflection in learning, there are significant theoretical, methodological, and analytical challenges posed by measuring, modeling, and supporting reflection. This paper presents results from a classroom study to investigate middle-school students’ reflection during inquiry-driven learning with Crystal Island, a game-based learning environment for middle-school microbiology. To collect evidence of reflection during game-based learning, we used embedded reflection prompts to elicit written reflections during students’ interactions with Crystal Island. Results from analysis of data from 105 students highlight relationships between features of students’ reflections and learning outcomes related to both science content knowledge and problem solving. We consider implications for building adaptive support in game-based learning environments to foster deep reflection and enhance learning, and we identify key features in students’ problem-solving actions and reflections that are predictive of reflection depth. These findings present a foundation for providing adaptive support for reflection during game-based learning.}, journal={LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE}, author={Carpenter, Dan and Cloude, Elizabeth and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2021}, pages={280–291} }
 @article{tian_wiggins_fahid_emerson_bounajim_smith_boyer_wiebe_mott_lester_2021, title={Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78270-2_63}, abstractNote={Modeling a learner’s frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, author={Tian, Xiaoyi and Wiggins, Joseph B. and Fahid, Fahmid Morshed and Emerson, Andrew and Bounajim, Dolly and Smith, Andy and Boyer, Kristy Elizabeth and Wiebe, Eric and Mott, Bradford and Lester, James}, year={2021}, pages={355–360} }
 @article{rachmatullah_reichsman_lord_dorsey_mott_lester_wiebe_2021, title={Modeling Secondary Students' Genetics Learning in a Game-Based Environment: Integrating the Expectancy-Value Theory of Achievement Motivation and Flow Theory}, volume={30}, ISSN={["1573-1839"]}, DOI={10.1007/s10956-020-09896-8}, number={4}, journal={JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY}, author={Rachmatullah, Arif and Reichsman, Frieda and Lord, Trudi and Dorsey, Chad and Mott, Bradford and Lester, James and Wiebe, Eric}, year={2021}, month={Aug}, pages={511–528} }
 @article{min_spain_saville_mott_brawner_johnston_lester_2021, title={Multidimensional Team Communication Modeling for Adaptive Team Training: A Hybrid Deep Learning and Graphical Modeling Framework}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78292-4_24}, abstractNote={Team communication modeling offers great potential for adaptive learning environments for team training. However, the complex dynamics of team communication pose significant challenges for team communication modeling. To address these challenges, we present a hybrid framework integrating deep learning and probabilistic graphical models that analyzes team communication utterances with respect to the intent of the utterance and the directional flow of communication within the team. The hybrid framework utilizes conditional random fields (CRFs) that use deep learning-based contextual, distributed language representations extracted from team members’ utterances. An evaluation with communication data collected from six teams during a live training exercise indicate that linear-chain CRFs utilizing ELMo utterance embeddings (1) outperform both multi-task and single-task variants of stacked bidirectional long short-term memory networks using the same distributed representations of the utterances, (2) outperform a hybrid approach that uses non-contextual utterance representations for the dialogue classification tasks, and (3) demonstrate promising domain-transfer capabilities. The findings suggest that the hybrid multidimensional team communication analysis framework can accurately recognize speaker intent and model the directional flow of team communication to guide adaptivity in team training environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Min, Wookhee and Spain, Randall and Saville, Jason D. and Mott, Bradford and Brawner, Keith and Johnston, Joan and Lester, James}, year={2021}, pages={293–305} }
 @article{emerson_henderson_min_rowe_minogue_lester_2021, title={Multimodal Trajectory Analysis of Visitor Engagement with Interactive Science Museum Exhibits}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78270-2_27}, abstractNote={Recent years have seen a growing interest in investigating visitor engagement in science museums with multimodal learning analytics. Visitor engagement is a multidimensional process that unfolds temporally over the course of a museum visit. In this paper, we introduce a multimodal trajectory analysis framework for modeling visitor engagement with an interactive science exhibit for environmental sustainability. We investigate trajectories of multimodal data captured during visitor interactions with the exhibit through slope-based time series analysis. Utilizing the slopes of the time series representations for each multimodal data channel, we conduct an ablation study to investigate how additional modalities lead to improved accuracy while modeling visitor engagement. We are able to enhance visitor engagement models by accounting for varying levels of visitors’ science fascination, a construct integrating science interest, curiosity, and mastery goals. The results suggest that trajectory-based representations of the multimodal visitor data can serve as the foundation for visitor engagement modeling to enhance museum learning experiences.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, author={Emerson, Andrew and Henderson, Nathan and Min, Wookhee and Rowe, Jonathan and Minogue, James and Lester, James}, year={2021}, pages={151–155} }
 @article{geden_emerson_carpenter_rowe_azevedo_lester_2021, title={Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection}, volume={31}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-020-00220-4}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Geden, Michael and Emerson, Andrew and Carpenter, Dan and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2021}, month={Mar}, pages={1–23} }
 @misc{rowe_lester_2020, title={Artificial Intelligence for Personalized Preventive Adolescent Healthcare}, volume={67}, ISSN={["1879-1972"]}, DOI={10.1016/j.jadohealth.2020.02.021}, abstractNote={Recent advances in artificial intelligence (AI) are creating new opportunities for personalizing technology-based health interventions to adolescents. This article provides a computer science perspective on how emerging AI technologies—intelligent learning environments, interactive narrative generation, user modeling, and adaptive coaching—can be utilized to model adolescent learning and engagement and deliver personalized support in adaptive health technologies. Many of these technologies have emerged from human-centered applications of AI in education, training, and entertainment. However, their application to improving healthcare, to date, has been comparatively limited. We illustrate the opportunities provided by AI-driven adaptive technologies for adolescent preventive healthcare by describing a vision of how future adolescent preventive health interventions might be delivered both inside and outside of the clinic. Key challenges posed by AI-driven health technologies are also presented, including issues of privacy, ethics, encoded bias, and integration into clinical workflows and adolescent lives. Examples of empirical findings about the effectiveness of AI technologies for user modeling and adaptive coaching are presented, which underscore their promise for application toward adolescent health. The article concludes with a brief discussion of future research directions for the field, which is well positioned to leverage AI to improve adolescent health and well-being.}, number={2}, journal={JOURNAL OF ADOLESCENT HEALTH}, author={Rowe, Jonathan P. and Lester, James C.}, year={2020}, month={Aug}, pages={552–558} }
 @article{saleh_chen_hmelo-silver_glazewski_mott_lester_2020, title={Coordinating scaffolds for collaborative inquiry in a game-based learning environment}, volume={57}, ISSN={["1098-2736"]}, DOI={10.1002/tea.21656}, abstractNote={Abstract}, number={9}, journal={JOURNAL OF RESEARCH IN SCIENCE TEACHING}, author={Saleh, Asmalina and Chen, Yuxin and Hmelo-Silver, Cindy E. and Glazewski, Krista D. and Mott, Bradford W. and Lester, James C.}, year={2020}, month={Nov}, pages={1490–1518} }
 @article{ozer_rowe_tebb_berna_penilla_giovanelli_jasik_lester_2020, title={Fostering Engagement in Health Behavior Change: Iterative Development of an Interactive Narrative Environment to Enhance Adolescent Preventive Health Services}, volume={67}, ISSN={["1879-1972"]}, DOI={10.1016/j.jadohealth.2020.04.022}, abstractNote={Accidents and unintentional injuries account for the greatest number of adolescent deaths, often involving use of alcohol and other substances. This article describes the iterative design and development of Interactive Narrative System for Patient-Individualized Reflective Exploration (INSPIRE), a narrative-centered behavior change environment for adolescents focused on reducing alcohol use. INSPIRE is designed to serve as an extension to clinical preventive care, engaging adolescents in a theoretically grounded intervention for health behavior change by leveraging 3D game engine and interactive narrative technologies.Adolescents were engaged in all aspects of the iterative, multiyear development process of INSPIRE through over 20 focus groups and iterative pilot testing involving more than 145 adolescents. Qualitative findings from focus groups are reported, as well as quantitative findings from small-scale pilot sessions investigating adolescent engagement with a prototype version of INSPIRE using a combination of questionnaire and interaction trace log data.Adolescents reported that they found INSPIRE to be engaging, believable, and relevant to their lives. The majority of participants indicated that the narrative's protagonist character was like them (84%) and that the narrative featured virtual characters that they could relate to (79%). In the interactive narrative, the goals most frequently chosen by adolescents were "stay in control" (60%) and "do not get in trouble" (55%).With a strong theoretical framework (social-cognitive behavior change theory) and technology advances (narrative-centered learning environments), the field is well positioned to design health behavior change systems that can realize significant impacts on behavior change for adolescent preventive health.}, number={2}, journal={JOURNAL OF ADOLESCENT HEALTH}, author={Ozer, Elizabeth M. and Rowe, Jonathan and Tebb, Kathleen P. and Berna, Mark and Penilla, Carlos and Giovanelli, Alison and Jasik, Carolyn and Lester, James C.}, year={2020}, month={Aug}, pages={S34–S44} }
 @article{ozer_lester_2020, title={Innovative Digital Technologies to Improve Adolescent and Young Adult Health}, volume={67}, ISSN={["1879-1972"]}, DOI={10.1016/j.jadohealth.2020.05.015}, abstractNote={The lives of adolescents and young adults (AYAs) have become increasingly intertwined with technology. Multidisciplinary perspectives and collaboration are needed to capitalize on the strategic use of technology during key developmental windows. Technology-rich models of behavior change, with opportunities for personalizing health interventions, offer significant transformative potential to improve adolescent and young adult health. There is considerable momentum behind advancing integration of digital health technology to enhance the efficiency and effectiveness of the clinical encounter, and rapid advances in technology provide mechanisms for enabling AYAs to take agentic roles in promoting health practice and policy. This Special Issue, Innovative Digital Technologies to Improve Adolescent and Young Adult Health, evolved from our collaborative multidisciplinary research that has been supported by the National Science Foundation under the Smart and Connected Health: Connecting Data, People, and Systems program (IIS-1344670 & IIS-1344803), with the goal of accelerating the development and integration of innovative technology to support the transformation of health and medicine. In the special issue, we are excited to share articles that highlight the potential of innovative technologies to promote AYA health and well-being. The need for the AYA health community to engage in multidisciplinary work to address key challenges posed by health technologies, including access, inequity, bias, privacy, security, and integration into clinical workflows and adolescent lives, is essential. This requires an intentional focus on inclusivity for all AYAs, especially those historically excluded, without which these technologies will reproduce existing inequalities and structural racism in health care. The rapid shift to online technology for clinical services delivery, research data collection, and education in response to the COVID-19 pandemic highlights the opportunities and perils of innovative techologies, particularly in regard to disparities in access, inequity, and privacy concerns. We are grateful to the guest editor of this Special Issue, Professor Lena Sanci, and the many authors who have contributed their work.}, number={2}, journal={JOURNAL OF ADOLESCENT HEALTH}, author={Ozer, Elizabeth M. and Lester, James C.}, year={2020}, month={Aug}, pages={S3–S3} }
 @article{emerson_cloude_azevedo_lester_2020, title={Multimodal learning analytics for game-based learning}, volume={51}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.12992}, abstractNote={Abstract}, number={5}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Emerson, Andrew and Cloude, Elizabeth B. and Azevedo, Roger and Lester, James}, year={2020}, month={Sep}, pages={1505–1526} }
 @article{taub_sawyer_lester_azevedo_2020, title={The Impact of Contextualized Emotions on Self-Regulated Learning and Scientific Reasoning during Learning with a Game-Based Learning Environment}, volume={30}, ISBN={1560-4306}, DOI={10.1007/s40593-019-00191-1}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Taub, Michelle and Sawyer, Robert and Lester, James and Azevedo, Roger}, year={2020}, month={Mar}, pages={97–120} }
 @article{henderson_rowe_mott_brawner_baker_lester_2019, title={4D Affect Detection: Improving Frustration Detection in Game-Based Learning with Posture-Based Temporal Data Fusion}, volume={11625}, ISBN={["978-3-030-23203-0"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-23204-7_13}, abstractNote={Recent years have seen growing interest in utilizing sensors to detect learner affect. Modeling frustration has particular significance because of its central role in learning. However, sensor-based affect detection poses important challenges. Motion-tracking cameras produce vast streams of spatial and temporal data, but relatively few systems have harnessed this data successfully to produce accurate run-time detectors of learner frustration outside of the laboratory. In this paper, we introduce a data-driven framework that leverages spatial and temporal posture data to detect learner frustration using deep neural network-based data fusion techniques. To train and validate the detectors, we utilize posture data collected with Microsoft Kinect sensors from students interacting with a game-based learning environment for emergency medical training. Ground-truth labels of learner frustration were obtained using the BROMP quantitative observation protocol. Results show that deep neural network-based late fusion techniques that combine spatial and temporal data yield significant improvements to frustration detection relative to baseline models.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I}, author={Henderson, Nathan L. and Rowe, Jonathan P. and Mott, Bradford W. and Brawner, Keith and Baker, Ryan and Lester, James C.}, year={2019}, pages={144–156} }
 @article{smith_leeman-munk_shelton_mott_wiebe_lester_2019, title={A Multimodal Assessment Framework for Integrating Student Writing and Drawing in Elementary Science Learning}, volume={12}, ISSN={1939-1382 2372-0050}, url={http://dx.doi.org/10.1109/TLT.2018.2799871}, DOI={10.1109/TLT.2018.2799871}, abstractNote={Science learning is inherently multimodal, with students utilizing both drawings and writings to explain observations of physical phenomena. As such assessments in science should accommodate the many ways students express their understanding, especially given evidence that understanding is distributed across both drawing and writing. In recent years advanced automated assessment techniques that evaluate expressive student artifacts have emerged. However, these techniques have largely operated individually, each considering only a single mode. We propose a framework for the multimodal automated assessment of students’ writing and drawing to leverage the synergies inherent across modalities and create a more complete and accurate picture of a student's knowledge. We introduce a multimodal assessment framework as well as two computational techniques for automatically analyzing student writings and drawings: a convolutional neural network-based model for assessing student writing, and a topology-based model for assessing student drawing. Evaluations with elementary students’ writings and drawings collected with a tablet-based digital science notebook demonstrate that 1) each of the framework's two modalities provide an independent and complementary measure of student science learning, and 2) the computational methods are capable of accurately assessing student work from both modalities and offer the potential for integration in technology-rich learning environments for real-time formative assessment.}, number={1}, journal={IEEE Transactions on Learning Technologies}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Smith, Andy and Leeman-Munk, Samuel and Shelton, Angi and Mott, Bradford and Wiebe, Eric and Lester, James}, year={2019}, month={Jan}, pages={3–15} }
 @article{saleh_hmelo-silver_glazewski_mott_chen_rowe_lester_2019, title={Collaborative inquiry play A design case to frame integration of collaborative problem solving with story-centric games}, volume={120}, ISSN={["1758-6909"]}, DOI={10.1108/ILS-03-2019-0024}, abstractNote={PurposeThis paper aims to present a model of collaborative inquiry play: rule-based imaginary situations that provide challenging problems and support agentic multiplayer interactions (c.f., Vygotsky, 1967; Salen and Zimmerman, 2003). Drawing on problem-based learning (PBL, Hmelo-Silver, 2004), this paper provides a design case to articulate the relationship between the design goals and the game-based learning environment.}, number={9/10}, journal={INFORMATION AND LEARNING SCIENCES}, author={Saleh, Asmalina and Hmelo-Silver, Cindy E. and Glazewski, Krista D. and Mott, Bradford and Chen, Yuxin and Rowe, Jonathan P. and Lester, James C.}, year={2019}, month={Oct}, pages={547–566} }
 @article{geden_smith_campbell_spain_amos-binks_mott_feng_lester_2019, title={Construction and Validation of an Anticipatory Thinking Assessment}, volume={10}, ISSN={["1664-1078"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85077306542&partnerID=MN8TOARS}, DOI={10.3389/fpsyg.2019.02749}, abstractNote={Anticipatory thinking is a critical cognitive skill for successfully navigating complex, ambiguous systems in which individuals must analyze system states, anticipate outcomes, and forecast future events. For example, in military planning, intelligence analysis, business, medicine, and social services, individuals must use information to identify warnings, anticipate a spectrum of possible outcomes, and forecast likely futures in order to avoid tactical and strategic surprise. Existing methods for examining anticipatory thinking skill have relied upon task-specific behavioral measures or are resource-intensive, both of which are challenging to scale. Given the increasing importance of anticipatory thinking in many domains, developing a generic assessment of this skill and identifying the underlying cognitive mechanisms supporting it are paramount. The work reported here focuses on the development and validation of the anticipatory thinking assessment (ANTA) for measuring the divergent generative process of anticipatory thinking. Two-hundred and ten participants completed the ANTA, which required them to anticipate possible risks, opportunities, trends, or other uncertainties associated with a focal topic. Responses to the anticipatory thinking and divergent thinking tasks were rated by trained raters on a five-point scale according to the uniqueness, specificity, and remoteness of responses. Results supported the ANTA’s construct validity, convergent validity, and discriminant validity. We also explored the relationship between the ANTA scores and certain psychological traits and cognitive measures (need for cognition, need for closure, and mindfulness). Our findings suggest that the ANTA is a psychometrically valid instrument that may help researchers investigate anticipatory thinking in new contexts.}, journal={FRONTIERS IN PSYCHOLOGY}, author={Geden, Michael and Smith, Andy and Campbell, James and Spain, Randall and Amos-Binks, Adam and Mott, Bradford W. and Feng, Jing and Lester, James}, year={2019}, month={Dec} }
 @article{mott_taylor_lee_rowe_saleh_glazewski_hmelo-silver_lester_2019, title={Designing and Developing Interactive Narratives for Collaborative Problem-Based Learning}, volume={11869}, ISBN={["978-3-030-33893-0"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-33894-7_10}, abstractNote={Narrative and collaboration are two core features of rich interactive learning. Narrative-centered learning environments offer significant potential for supporting student learning. By contextualizing learning within interactive narratives, these environments leverage students’ innate facilities for developing understandings through stories. Computer-supported collaborative learning environments offer students rich, collaborative learning experiences in which small groups of students engage in constructing artifacts, addressing disciplinary challenges, and solving problems. Narrative and collaboration have distinct affordances for learning, but combining them poses significant challenges. In this paper, we present initial work on solving this problem by introducing collaborative narrative-centered learning environments. These environments will enable small groups of students to collaboratively solve problems in rich multi-participant storyworlds. We propose a novel framework for designing and developing these environments, which we are using to create a collaborative narrative-centered learning environment for middle school ecosystems education. In the learning environment, students work on problem-solving scenarios centered on how to support optimal fish health in aquatic environments. Results from pilot testing the learning environment with 45 students suggest it supports the creation of engaging and effective collaborative narrative-centered learning experiences.}, journal={INTERACTIVE STORYTELLING, ICIDS 2019}, author={Mott, Bradford W. and Taylor, Robert G. and Lee, Seung Y. and Rowe, Jonathan P. and Saleh, Asmalina and Glazewski, Krista D. and Hmelo-Silver, Cindy E. and Lester, James C.}, year={2019}, pages={86–100} }
 @article{ozer_rowe_tebb_berna_jasik_penilla_giovanelli_ozer-staton_kellenberger_lester_2019, title={ENGAGING ADOLESCENTS IN A SELF-ADAPTIVE PERSONALIZED BEHAVIOR CHANGE SYSTEM FOR ADOLESCENT PREVENTIVE HEALTHCARE}, volume={64}, DOI={10.1016/j.jadohealth.2018.10.132}, abstractNote={Although adolescent health risk behaviors are amenable to behavioral intervention, few health information technology interventions have been integrated into adolescent care. The objective of this research was to design, implement, and investigate INSPIRE, a self-adaptive personalized behavior change system for adolescent preventive health with a focus on alcohol use. INSPIRE was developed by a transdisciplinary team of psychologists, health services researchers, and computer scientists. With linkages through primary care, adolescents will interact with INSPIRE outside the clinic to extend the reach of the clinician and promote behavior change. Using the social cognitive theory of behavior change, INSPIRE has been developed in an iterative process with input from adolescents on characters’ personae and avatars, virtual environments, and narratives. In INSPIRE, adolescents adopt the role of a teenage protagonist who “relives” the events and decisions of a high-school social gathering involving alcohol use. Adolescents interact with a cast of virtual characters who model a broad range of health behaviors. The user is an active participant in a dynamically unfolding narrative that addresses issues of peer pressure, social norms, and alternative consequences of alcohol use, with outcomes and storyline actively shaped by the user’s decisions. The virtual environment was designed to enhance adolescents’ knowledge, personal efficacy, and self-regulatory processes. Over a 5-year period, the INSPIRE team has conducted 20 focus groups and pilot tests with a diverse group of approximately 200 adolescents to obtain feedback and refine INSPIRE’s virtual environment. During March 2018, 20 adolescents aged 14 to 18 (10 female 8 male and 1 trans male) participated in two San Francisco based pilot tests of INSPIRE's most updated first episode. Both adolescent survey and computerized log data were collected in order to measure user engagement. Pilot test participants found the virtual environment to be relatable, germane, and engaging. All teens completed the narrative episode, spending an average of 23 minutes engrossed in gameplay. Nearly 80% found the protagonist to be either very much or somewhat like them and 80% indicated that the narrative included characters that they could relate to. A majority of adolescents (65%) reported playing as themselves, in terms of the problem-solving decisions they made in the virtual environment. Moreover, 82% of adolescents found game play interesting, over half found it to be rewarding, and they were highly interested in wanting to know how future episodes ended. We have utilized an iterative development process involving regular engagement with youth to create a prototype narrative-centered behavior change environment for adolescent preventive health with a focus on risky behavior and alcohol use. Initial pilot-tests have indicated that adolescents find the narrative-centered behavior change environment to be engaging, believable, and relevant to their lives, while providing practice opportunities that can be translated to situations adolescents encounter in their own lives.}, number={2}, journal={JOURNAL OF ADOLESCENT HEALTH}, author={Ozer, Elizabeth M. and Rowe, Jonathan P. and Tebb, Kathleen P. and Berna, Mark S. and Jasik, Carolyn Bradner and Penilla, Carlos and Giovanelli, Alison S. and Ozer-Staton, Max and Kellenberger, Averie Lee and Lester, James C.}, year={2019}, pages={S60–61} }
 @article{ozer_penilla_spain_mott_woodson_lester_2019, title={HEALTH QUEST: PROMOTING ADOLESCENTS' HEALTH SCIENCE CAREER INTERESTS THROUGH TECHNOLOGY-RICH LEARNING EXPERIENCES}, volume={64}, ISSN={["1879-1972"]}, DOI={10.1016/j.jadohealth.2018.10.279}, abstractNote={The purpose of the Health Quest project is to create an intelligent game-based learning environment and associated resources to increase adolescents' knowledge of, interest in and self-efficacy to pursue health science careers. Science, technology, engineering and mathematics (STEM) fields are among the fastest growing career opportunities, yet women and ethnic minorities remain underrepresented in these fields. As adolescence offers a key window to promote interest in and increase self-efficacy to pursue health research careers and the health professions, Health Quest focuses on an adolescent student population. Health Quest will be pilot tested and used in classrooms across North Carolina and California, and will also be featured in informal learning settings including the North Carolina Museum of Natural Sciences and in after-school programs in San Francisco. To support the project’s first aim of designing and developing a series of Health Quest Career Adventure Game episodes and interactive video interviews to promote students’ interest in health science careers, the project team conducted an extensive review of recent NIH Science Education Partnership Award (SEPA) projects awarded over the past three years. The purpose of the review was to identify existing programs and online resources that have been developed to promote students’ interest in health science careers and to identify any unique resources or game-based learning environments that aim to promote self-efficacy or mastery experiences towards building competency and interest in health science career fields. We conducted a review of existing SEPA projects awarded from 2015 to 2018 to identify online resources and game-based learning experiences that have been designed to promote students' interest in health science careers. Projects were reviewed according to content focus, instructional approach and resources provided (e.g., websites, podcasts, videos and online-games). Forty five of the 48 SEPA projects reviewed had websites. Projects focused primarily on the life sciences, such as genomics. A majority of projects involved engaging students in the classroom, including providing opportunities in the lab, and used an experiential and mentor-based approach to provide students with unique hands-on learning opportunities to promote students’ knowledge and interest in STEM fields. Other projects involved teacher development. Game-based learning technologies offer significant potential for increasing students’ interest in health science careers. The results of our review showed that while SEPA programs focus on promoting interests and STEM opportunities by engaging students in hands-on classroom and laboratory-based activities, there is a lack of technology-based resources that can be used to extend outreach to students who would not otherwise have access to these programs. We hypothesize that by leveraging the intrinsic motivation of game-play, participant agency, and personalized learning, we can create engaging learning experiences that enable students to explore and gain confidence in pursuing health science careers.}, number={2}, journal={JOURNAL OF ADOLESCENT HEALTH}, author={Ozer, Elizabeth M. and Penilla, Carlos and Spain, Randall D. and Mott, Bradford W. and Woodson, Donald and Lester, James C.}, year={2019}, month={Feb}, pages={S134–S134} }
 @article{min_park_wiggins_mott_wiebe_boyer_lester_2019, title={Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068335512&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-23207-8_37}, abstractNote={Recent years have seen a growing interest in conversational pedagogical agents. However, creating robust dialogue managers for conversational pedagogical agents poses significant challenges. Agents’ misunderstandings and inappropriate responses may cause breakdowns in conversational flow, lead to breaches of trust in agent-student relationships, and negatively impact student learning. Dialogue breakdown detection (DBD) is the task of predicting whether an agent’s utterance will cause a breakdown in an ongoing conversation. A robust DBD framework can support enhanced user experiences by choosing more appropriate responses, while also offering a method to conduct error analyses and improve dialogue managers. This paper presents a multimodal deep learning-based DBD framework to predict breakdowns in student-agent conversations. We investigate this framework with dialogues between middle school students and a conversational pedagogical agent in a game-based learning environment. Results from a study with 92 middle school students demonstrate that multimodal long short-term memory network (LSTM)-based dialogue breakdown detectors incorporating eye gaze features achieve high predictive accuracies and recall rates, suggesting that multimodal detectors can play an important role in designing conversational pedagogical agents that effectively engage students in dialogue.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Min, Wookhee and Park, Kyungjin and Wiggins, Joseph and Mott, Bradford and Wiebe, Eric and Boyer, Kristy Elizabeth and Lester, James}, year={2019}, pages={195–200} }
 @article{wiggins_kulkarni_min_boyer_mott_wiebe_lester_2019, title={Take the Initiative: Mixed Initiative Dialogue Policies for Pedagogical Agents in Game-Based Learning Environments}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068350756&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-23207-8_58}, abstractNote={Pedagogical agents have been shown to be highly effective for supporting learning in a broad range of contexts, including game-based learning. However, there are key open questions around how to design dialogue policies for pedagogical agents that support students in game-based learning environments. This paper reports on a study to investigate two different agent dialogue policies with regard to conversational initiative, a core consideration in dialogue system design. In the User Initiative policy, only the student could initiate conversations with the agent, while in the Mixed Initiative policy, both the agent and the student could initiate conversations. In a study with 67 college students, results showed that the Mixed Initiative policy not only promoted more conversation, but also better supported the goals of the game-based learning environment by fostering exploration, yielding better performance on in-game assessments, and creating higher student engagement.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Wiggins, Joseph B. and Kulkarni, Mayank and Min, Wookhee and Boyer, Kristy Elizabeth and Mott, Bradford and Wiebe, Eric and Lester, James}, year={2019}, pages={314–318} }
 @article{cloude_taub_lester_azevedo_2019, title={The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-23207-8_7}, abstractNote={To examine relations between achievement goal orientation—a construct of motivation, metacognition and learning, multiple data channels were collected from 58 students while problem solving in a game-based learning environment. Results suggest students with different goal orientations use metacognitive processes differently but found no differences in learning. Findings have implications for measuring motivation using multiple data channels to design adaptive game-based learning environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Cloude, Elizabeth B. and Taub, Michelle and Lester, James and Azevedo, Roger}, year={2019}, pages={36–40} }
 @article{loderer_pekrun_lester_2018, title={Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments}, ISSN={0959-4752}, url={http://dx.doi.org/10.1016/J.LEARNINSTRUC.2018.08.002}, DOI={10.1016/J.LEARNINSTRUC.2018.08.002}, abstractNote={Understanding emotions in technology-based learning environments (TBLEs) has become a paramount goal across different research communities, but to date, these have operated in relative isolation. Based on control-value theory (Pekrun, 2006), we reviewed 186 studies examining emotions in TBLEs that were published between 1965 and 2018. We extracted effect sizes quantifying relations between emotions (enjoyment, curiosity/interest, anxiety, anger/frustration, confusion, boredom) and their antecedents (control-value appraisals, prior knowledge, gender, TBLE characteristics) and outcomes (engagement, learning strategies, achievement). Mean effects largely supported hypotheses (e.g., positive relations between enjoyment and appraisals, achievement, and cognitive support) and remained relatively stable across moderators. These findings imply that levels of emotions differ across TBLEs, but that their functional relations with appraisals and learning are equivalent across environments. Implications for research and designing emotionally sound TBLEs are discussed.}, journal={Learning and Instruction}, publisher={Elsevier BV}, author={Loderer, Kristina and Pekrun, Reinhard and Lester, James C.}, year={2018}, month={Nov}, pages={101162} }
 @article{defalco_rowe_paquette_georgoulas-sherry_brawner_mott_baker_lester_2018, title={Detecting and Addressing Frustration in a Serious Game for Military Training}, volume={28}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-017-0152-1}, abstractNote={Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate responses to affect. Recent work has suggested that affect detection is more effective when both physical sensors and interaction logs are used, and that context-sensitive design of affective feedback is necessary to enhance engagement and improve learning. In this paper, we provide a comprehensive report on a multi-part study that integrates detection, validation, and intervention into a unified approach. This paper examines the creation of both sensor-based and interaction-based detectors of student affect, producing successful detectors of student affect. In addition, it reports results from an investigation of motivational feedback messages designed to address student frustration, and investigates whether linking these interventions to detectors improves outcomes. Our results are mixed, finding that self-efficacy enhancing interventions based on interaction-based affect detectors enhance outcomes in one of two experiments investigating affective interventions. This work is conducted in the context of the GIFT framework for intelligent tutoring, and the TC3Sim game-based simulation that provides training for first responder skills.}, number={2}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={DeFalco, Jeanine A. and Rowe, Jonathan P. and Paquette, Luc and Georgoulas-Sherry, Vasiliki and Brawner, Keith and Mott, Bradford W. and Baker, Ryan S. and Lester, James C.}, year={2018}, month={Jun}, pages={152–193} }
 @article{emerson_sawyer_azevedo_lester_2018, title={Gaze-Enhanced Student Modeling for Game-based Learning}, DOI={10.1145/3209219.3209238}, abstractNote={Recent advances in eye-tracking technologies have introduced the opportunity to incorporate gaze into student modeling. Creating student models that leverage gaze information holds significant promise for game-based learning environments. This paper introduces a gaze-enhanced student modeling framework that incorporates student eye tracking to dynamically predict students' performance in a game-based learning environment for microbiology education, CRYSTAL ISLAND. The gaze-enhanced student modeling framework was investigated in a study comparing a gaze-enhanced student model with a baseline student model that does not utilize student eye-tracking. Results of a study conducted with 65 college students interacting with the CRYSTAL ISLAND game-based learning environment indicate that the gaze-enhanced student model significantly outperforms the baseline model in dynamically predicting student problem-solving performance. The findings suggest that incorporating gaze into student modeling can contribute to a new generation of student models for game-based learning environments.}, journal={PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18)}, author={Emerson, Andrew and Sawyer, Robert and Azevedo, Roger and Lester, James}, year={2018}, pages={63–72} }
 @inproceedings{mudrick_sawyer_price_lester_roberts_azevedo_2018, title={Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH}, volume={10858}, ISBN={0}, DOI={10.1007/978-3-319-91464-0_14}, abstractNote={Students need to accurately monitor and judge the difficulty of learning materials to effectively self-regulate their learning with advanced learning technologies such as intelligent tutoring systems (ITSs), including MetaTutorIVH. However, there is a paucity of research examining how metacognitive monitoring processes such as ease of learning (EOLs) judgments can be used to provide adaptive scaffolding and predict student performance during learning ITSs. In this paper, we report on a study investigating how students’ EOL judgments can influence their performance and significantly predict their learning outcomes during learning with MetaTutorIVH, an ITS for human physiology. The results have important design implications for incorporating different types of metacognitive judgements in student models to support metacognition and foster learning of complex ITSs.}, booktitle={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Mudrick, Nicholas V. and Sawyer, Robert and Price, Megan J. and Lester, James and Roberts, Candice and Azevedo, Roger}, year={2018}, pages={140–149} }
 @inbook{sawyer_mudrick_azevedo_lester_2018, title={Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies}, ISBN={9783319938455 9783319938462}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-93846-2_58}, DOI={10.1007/978-3-319-93846-2_58}, abstractNote={Affect and metacognition play a central role in learning. We examine the relationships between students’ affective state dynamics, metacognitive judgments, and performance during learning with MetaTutorIVH, an advanced learning technology for human biology education. Student emotions were tracked using facial expression recognition embedded within MetaTutorIVH and transitions between emotions theorized to be important to learning (e.g., confusion, frustration, and joy) are analyzed with respect to likelihood of occurrence. Transitions from confusion to frustration were observed at a significantly high likelihood, although no differences in performance were observed in the presence of these affective states and transitions. Results suggest that the occurrence of emotions have a significant impact on students’ retrospective confidence judgments, which they made after submitting their answers to multiple-choice questions. Specifically, the presence of confusion and joy during learning had a positive impact on student confidence in their performance while the presence of frustration and transition from confusion to frustration had a negative impact on confidence, even after accounting for individual differences in multiple-choice confidence.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Sawyer, Robert and Mudrick, Nicholas V. and Azevedo, Roger and Lester, James}, year={2018}, pages={312–316} }
 @article{catete_lytle_dong_boulden_akram_houchins_barnes_wiebe_lester_mott_et al._2018, title={Infusing Computational Thinking into Middle Grade Science Classrooms: Lessons Learned}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85056713650&partnerID=MN8TOARS}, DOI={10.1145/3265757.3265778}, abstractNote={There is a growing need to present all students with an opportunity to learn computer science and computational thinking (CT) skills during their primary and secondary education. Traditionally, these opportunities are available outside of the core curriculum as stand-alone courses often taken by those with preparatory privilege. Researchers have identified the need to integrate CT into core classes to provide equitable access to these critical skills. We have worked in a research-practice partnership with two magnet middle schools focused on digital sciences to develop and implement computational thinking into life sciences classes. In this report, we present initial lessons learned while conducting our design-based implementation research on integrating computational thinking into middle school science classes. These case studies suggest that several factors including teacher engagement, teacher attitudes, student prior experience with CS/CT, and curriculum design can all impact student engagement in integrated science-CT lessons.}, journal={WIPSCE'18: PROCEEDINGS OF THE 13TH WORKSHOP IN PRIMARY AND SECONDARY COMPUTING EDUCATION}, publisher={ACM Press}, author={Catete, Veronica and Lytle, Nicholas and Dong, Yihuan and Boulden, Danielle and Akram, Bita and Houchins, Jennifer and Barnes, Tiffany and Wiebe, Eric and Lester, James and Mott, Bradford and et al.}, year={2018}, pages={109–114} }
 @inproceedings{buffum_ying_zheng_boyer_wiebe_mott_blackburn_lester_2018, title={Introducing the Computer Science Concept of Variables in Middle School Science Classrooms}, ISBN={9781450351034}, url={http://dx.doi.org/10.1145/3159450.3159545}, DOI={10.1145/3159450.3159545}, abstractNote={The K-12 Computer Science Framework has established that students should be learning about the computer science concept of variables as early as middle school, although the field has not yet determined how this and other related concepts should be introduced. Secondary school computer science curricula such as Exploring CS and AP CS Principles often teach the concept of variables in the context of algebra, which most students have already encountered in their mathematics courses. However, when strategizing how to introduce the concept at the middle school level, we confront the reality that many middle schoolers have not yet learned algebra. With that challenge in mind, this position paper makes a case for introducing the concept of variables in the context of middle school science. In addition to an analysis of existing curricula, the paper includes discussion of a day-long pilot study and the consequent teacher feedback that further supports the approach. The CS For All initiative has increased interest in bringing computer science to middle school classrooms; this paper makes an argument for doing so in a way that can benefit students' learning of both computer science and core science content.}, booktitle={Proceedings of the 49th ACM Technical Symposium on Computer Science Education - SIGCSE '18}, publisher={ACM Press}, author={Buffum, Philip Sheridan and Ying, Kimberly Michelle and Zheng, Xiaoxi and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Mott, Bradford W. and Blackburn, David C. and Lester, James C.}, year={2018}, pages={906–911} }
 @article{sottilare_baker_graesser_lester_2018, title={Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research}, volume={28}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-017-0149-9}, abstractNote={The Generalized Intelligent Framework for Tutoring (GIFT) is a research prototype with three general goals associated with its functions and components: 1) lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) provide effective adaptive instruction tailored to the needs of each individual learner or team of learners; and 3) provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices. This special issue focuses primarily on the third goal, GIFT as a research testbed. A discussion thread covers each article within this special issue and discusses its actual and potential impact on GIFT as a research tool for AIED. Our primary motivation was to introduce the AIED community to GIFT not just as a research tool, but as an extension of familiar challenges taken on previously by AIED scientists and practitioners. This preface provides a high level overview of the GIFT functions (authoring, instructional delivery and management, and experimentation) and presents its primary design principles. To learn more about GIFT, freely access the software, documentation, and associated technical papers visit www.GIFTtutoring.org .}, number={2}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Sottilare, Robert A. and Baker, Ryan S. and Graesser, Arthur C. and Lester, James C.}, year={2018}, month={Jun}, pages={139–151} }
 @article{geden_smith_campbell_amos-binks_mott_feng_lester_2018, title={Towards Adaptive Support for Anticipatory Thinking}, DOI={10.1145/3183654.3183665}, abstractNote={Adaptive training and support technologies have been used to improve training and performance in a number of domains. However, limited work on adaptive training has examined anticipatory thinking, which is the deliberate, divergent exploration and analysis of relevant futures to avoid surprise. Anticipatory thinking engages the process of imagining how uncertainties impact the future, helps identify leading indicators and causal dependencies of future scenarios, and complements forecasting, which focuses on assessing the likelihood of outcomes. It is particularly important for intelligence analysis, mission planning, and strategic forecasting, wherein practitioners apply prospective sense-making, scenario planning, and other methodologies to identify possible options and their effects during decision making processes. However, there is currently no underlying cognitive theory supporting specific anticipatory thinking methodologies, no adaptive technologies to support their training, and no existing measures to assess their efficacy. We are engaged in an ongoing effort to design adaptive technologies to support the acquisition and measurement of anticipatory thinking. As a first step toward adaptive environments that support the acquisition and application of anticipatory thinking competencies, we have developed a task to measure anticipatory thinking in which participants explore uncertainties and the impacts on the future given a particular topic. We present preliminary results from a study to examine the validity of this measure and discuss multiple factors that affect anticipatory thinking including attention, inhibitory control, need for cognition, need for closure, convergent thinking, and divergent thinking. We then introduce design principles for supporting training, application, and assessment of anticipatory thinking.}, journal={PROCEEDINGS OF THE TECHNOLOGY, MIND, AND SOCIETY CONFERENCE (TECHMINDSOCIETY'18)}, author={Geden, Michael and Smith, Andy and Campbell, James and Amos-Binks, Adam and Mott, Bradford and Feng, Jing and Lester, James}, year={2018} }
 @article{taub_azevedo_bradbury_millar_lester_2018, title={Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment}, volume={54}, ISSN={["0959-4752"]}, DOI={10.1016/j.learninstruc.2017.08.005}, abstractNote={The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with Crystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern mining and differential sequence mining on 64 undergraduate participants’ hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer sequences of testing items overall, and significantly lower instance support values of the PartiallyRelevant--Relevant to Relevant--Relevant and PartiallyRelevant--PartiallyRelevant to Relevant--Partially Relevant sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.}, journal={LEARNING AND INSTRUCTION}, author={Taub, Michelle and Azevedo, Roger and Bradbury, Amanda E. and Millar, Garrett C. and Lester, James}, year={2018}, month={Apr}, pages={93–103} }
 @article{pezzullo_wiggins_frankosky_min_boyer_mott_wiebe_lester_2017, title={"Thanks Alisha, Keep in Touch": Gender Effects and Engagement with Virtual Learning Companions}, volume={10331}, ISBN={["978-3-319-61424-3"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85022211435&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-61425-0_25}, abstractNote={Virtual learning companions have shown significant potential for supporting students. However, there appear to be gender differences in their effectiveness. In order to support all students well, it is important to develop a deeper understanding of the role that student gender plays during interactions with learning companions. This paper reports on a study to explore the impact of student gender and learning companion design. In a three-condition study, we examine middle school students’ interactions in a game-based learning environment that featured one of the following: (1) a learning companion deeply integrated into the narrative of the game; (2) a learning companion whose backstory and personality were not integrated into the narrative but who provided equivalent task support; and (3) no learning companion. The results show that girls were significantly more engaged than boys, particularly with the narrative-integrated agent, while boys reported higher mental demand with that agent. Even when controlling for video game experience and prior knowledge, the gender effects held. These findings contribute to the growing understanding that learning companions must adapt to students’ gender in order to facilitate the most effective learning interactions.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017}, author={Pezzullo, Lydia G. and Wiggins, Joseph B. and Frankosky, Megan H. and Min, Wookhee and Boyer, Kristy Elizabeth and Mott, Bradford W. and Wiebe, Eric N. and Lester, James C.}, year={2017}, pages={299–310} }
 @inbook{ocumpaugh_andres_baker_defalco_paquette_rowe_mott_lester_georgoulas_brawner_et al._2017, title={Affect Dynamics in Military Trainees Using vMedic: From Engaged Concentration to Boredom to Confusion}, ISBN={9783319614243 9783319614250}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-61425-0_20}, DOI={10.1007/978-3-319-61425-0_20}, abstractNote={The role of affect in learning has received increasing attention from AIED researchers seeking to understand how emotion and cognition interact in learning contexts. The dynamics of affect over time have been explored in a variety of research environments, allowing researchers to determine the extent to which common patterns are captured by hypothesized models. This paper present an analysis of affect dynamics among learners using vMedic, which teaches combat medicine protocols as part of the military training at West Point, the United States Military Academy. In doing so, we seek both to broaden the variety of learning contexts being explored in order better understand differences in these patterns and to test the theoretical predictions on the development of affect over time.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Ocumpaugh, Jaclyn and Andres, Juan Miguel and Baker, Ryan and DeFalco, Jeanine and Paquette, Luc and Rowe, Jonathan and Mott, Bradford and Lester, James and Georgoulas, Vasiliki and Brawner, Keith and et al.}, year={2017}, pages={238–249} }
 @inbook{sawyer_rowe_lester_2017, title={Balancing Learning and Engagement in Game-Based Learning Environments with Multi-objective Reinforcement Learning}, ISBN={9783319614243 9783319614250}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-61425-0_27}, DOI={10.1007/978-3-319-61425-0_27}, abstractNote={Game-based learning environments create rich learning experiences that are both effective and engaging. Recent years have seen growing interest in data-driven techniques for tutorial planning, which dynamically personalize learning experiences by providing hints, feedback, and problem scenarios at run-time. In game-based learning environments, tutorial planners are designed to adapt gameplay events in order to achieve multiple objectives, such as enhancing student learning or student engagement, which may be complementary or competing aims. In this paper, we introduce a multi-objective reinforcement learning framework for inducing game-based tutorial planners that balance between improving learning and engagement in game-based learning environments. We investigate a model-based, linear-scalarized multi-policy algorithm, Convex Hull Value Iteration, to induce a tutorial planner from a corpus of student interactions with a game-based learning environment for middle school science education. Results indicate that multi-objective reinforcement learning creates policies that are more effective at balancing multiple reward sources than single-objective techniques. A qualitative analysis of select policies and multi-objective preference vectors shows how a multi-objective reinforcement learning framework shapes the selection of tutorial actions during students’ game-based learning experiences to effectively achieve targeted learning and engagement outcomes.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Sawyer, Robert and Rowe, Jonathan and Lester, James}, year={2017}, pages={323–334} }
 @article{pruden_kerkhoff_spires_lester_2017, title={Enhancing Writing Achievement Through a Digital Learning Environment: Case Studies of Three Struggling Adolescent Male Writers}, volume={33}, ISSN={["1521-0693"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84964495128&partnerID=MN8TOARS}, DOI={10.1080/10573569.2015.1059780}, abstractNote={ABSTRACT The aim of this study was to explore how Narrative Theatre, a narrative-centered digital learning environment, supported the writing processes of 3 struggling adolescent male writers. We utilized a multicase study approach to capture 3 sixth-grade participants’ experiences with the digital learning environment before, during, and after writing. The case studies provided detailed portraits of the writers as well as insights into their digital writing processes related to student interest, student ability, and value for writing. The across-case analysis revealed 3 themes (i.e., choice, scaffolding, and self-efficacy) that illustrated how the digital learning environment contributed to the students’ writing experiences. Future research and development will focus on the addition of text animation for student products and the degree to which this feature further contributes to engagement and proficiency with struggling writers.}, number={1}, journal={READING & WRITING QUARTERLY}, author={Pruden, Manning and Kerkhoff, Shea N. and Spires, Hiller A. and Lester, James}, year={2017}, pages={1–19} }
 @article{min_frankosky_mott_wiebe_boyer_lester_2017, title={Inducing Stealth Assessors from Game Interaction Data}, volume={10331}, ISBN={["978-3-319-61424-3"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85022230700&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-61425-0_18}, abstractNote={A key untapped feature of game-based learning environments is their capacity to generate a rich stream of fine-grained learning interaction data. The learning behaviors captured in these data provide a wealth of information on student learning, which stealth assessment can utilize to unobtrusively draw inferences about student knowledge to provide tailored problem-solving support. In this paper, we present a long short-term memory network (LSTM)-based stealth assessment framework that takes as input an observed sequence of raw game-based learning environment interaction data along with external pre-learning measures to infer students’ post-competencies. The framework is evaluated using data collected from 191 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors induced from student game-based learning interaction data outperform comparable models that required labor-intensive hand-engineering of input features. The findings suggest that the LSTM-based approach holds significant promise for evidence modeling in stealth assessment.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017}, author={Min, Wookhee and Frankosky, Megan H. and Mott, Bradford W. and Wiebe, Eric N. and Boyer, Kristy Elizabeth and Lester, James C.}, year={2017}, pages={212–223} }
 @inbook{sawyer_smith_rowe_azevedo_lester_2017, title={Is More Agency Better? The Impact of Student Agency on Game-Based Learning}, volume={10331 LNAI}, ISBN={9783319614243 9783319614250}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-61425-0_28}, DOI={10.1007/978-3-319-61425-0_28}, abstractNote={Student agency has long been viewed as a critical element in game-based learning. Agency refers to the degree of freedom and control that a student has to perform meaningful actions in a learning environment. While long postulated to be central to student self-regulation, there is limited evidence on the design of game-based learning environments that promote student agency and its effect on learning. This paper reports on an experiment to investigate the impact of student agency on learning and problem-solving behavior in a game-based learning environment for microbiology. Students interacted with one of three versions of the system. In the High Agency condition, students could freely navigate the game’s 3D open-world environment and perform problem-solving actions in any order they chose. In the Low Agency condition, students were required to traverse the environment and solve the mystery in a prescribed partially ordered sequence. In the No Agency condition, students watched a video of an expert playing the game by following an “ideal path” for solving the problem scenario. Results indicate that students in the Low Agency condition achieved greater learning gains than students in both the High Agency and No Agency conditions, but exhibited more unproductive behaviors, suggesting that artfully striking a balance between high and low agency best supports learning.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Sawyer, Robert and Smith, Andy and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2017}, pages={335–346} }
 @article{rowe_lobene_mott_lester_2017, title={Play in the Museum: Design and Development of a Game-Based Learning Exhibit for Informal Science Education}, volume={9}, ISSN={["1942-3896"]}, DOI={10.4018/ijgcms.2017070104}, abstractNote={Digital games have been found to yield effective and engaging learning experiences across a broad range of subjects. Much of this research has been conducted in laboratory and K-12 classrooms. Recent advances in game technologies are expanding the range of educational contexts where game-based learning environments can be deployed, including informal settings such as museums and science centers. In this article, the authors describe the design, development, and formative evaluation of Future Worlds, a prototype game-based exhibit for collaborative explorations of sustainability in science museums. They report findings from a museum pilot study that investigated the influence of visitors' individual differences on learning and engagement. Results indicate that visitors showed significant gains in sustainability knowledge as well as high levels of engagement in a free-choice learning environment with Future Worlds. These findings point toward the importance of designing game-based learning exhibits that address the distinctive design challenges presented by museum settings.}, number={3}, journal={INTERNATIONAL JOURNAL OF GAMING AND COMPUTER-MEDIATED SIMULATIONS}, author={Rowe, Jonathan P. and Lobene, Eleni V. and Mott, Bradford W. and Lester, James C.}, year={2017}, pages={96–113} }
 @inproceedings{mudrick_taub_azevedo_rowe_lester_2017, title={Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion}, DOI={10.1109/acii.2017.8273598}, abstractNote={Affective support can play a central role in adaptive learning environments. Although virtual human tutors hold significant promise for providing affective support, a key open question is how a tutor's facial expressions can influence learners' performance. In this paper, we report on a study to examine the influence of a human tutor agent's facial expressions on learners' performance and emotions during learning. Results from the study suggest that learners' performance is significantly better when a human tutor agent facially expresses emotions that are congruent with the content relevancy. Results also suggest that learners facially express significantly more confusion when the human tutor agent provides incongruent facial expressions. These results can inform the design of virtual humans as pedagogical agents can inform the design of virtual humans as pedagogical agents and designing intelligent learner-agent interactions.}, booktitle={International conference on affective computing and intelligent}, author={Mudrick, N. V. and Taub, M. and Azevedo, R. and Rowe, J. and Lester, J.}, year={2017}, pages={184–189} }
 @article{taub_mudrick_azevedo_millar_rowe_lester_2017, title={Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with CRYSTAL ISLAND}, volume={76}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2017.01.038}, abstractNote={Game-based learning environments (GBLEs) have been touted as the solution for failing educational outcomes. In this study, we address some of these major issues by using multi-level modeling with data from eye movements and log files to examine the cognitive and metacognitive self-regulatory processes used by 50 college students as they read books and completed the associated in-game assessments (concept matrices) while playing the Crystal Island game-based learning environment. Results revealed that participants who read fewer books in total, but read each of them more frequently, and who had low proportions of fixations on books and concept matrices exhibited the strongest performance. Results stress the importance of assessing quality vs. quantity during gameplay, such that it is important to read books in-depth (i.e., quality), compared to reading books once (i.e., quantity). Implications for these findings involve designing adaptive GBLEs that scaffold participants based on their trace data, such that we can model efficient behaviors that lead to successful performance.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Taub, Michelle and Mudrick, Nicholas V. and Azevedo, Roger and Millar, Garrett C. and Rowe, Jonathan and Lester, James}, year={2017}, month={Nov}, pages={641–655} }
 @inbook{wang_rowe_mott_lester_2016, title={Decomposing Drama Management in Educational Interactive Narrative: A Modular Reinforcement Learning Approach}, ISBN={9783319482781 9783319482798}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-48279-8_24}, DOI={10.1007/978-3-319-48279-8_24}, abstractNote={Recent years have seen growing interest in data-driven approaches to personalized interactive narrative generation and drama management. Reinforcement learning (RL) shows particular promise for training policies to dynamically shape interactive narratives based on corpora of player-interaction data. An important open question is how to design reinforcement learning-based drama managers in order to make effective use of player interaction data, which is often expensive to gather and sparse relative to the vast state and action spaces required by drama management. We investigate an offline optimization framework for training modular reinforcement learning-based drama managers in an educational interactive narrative, Crystal Island. We leverage importance sampling to evaluate drama manager policies derived from different decompositional representations of the interactive narrative. Empirical results show significant improvements in drama manager quality from adopting an optimized modular RL decomposition compared to competing representations.}, booktitle={Interactive Storytelling}, publisher={Springer International Publishing}, author={Wang, Pengcheng and Rowe, Jonathan and Mott, Bradford and Lester, James}, year={2016}, pages={270–282} }
 @article{ozer_jasik_tebb_erenrich_berna_rowe_mott_lester_2016, title={Development of a Self-Adaptive Personalized Behavior Change System for Adolescent Preventive Healthcare}, volume={58}, ISSN={1054-139X}, url={http://dx.doi.org/10.1016/J.JADOHEALTH.2015.10.152}, DOI={10.1016/J.JADOHEALTH.2015.10.152}, abstractNote={Alcohol use is a leading cause of morbidity and mortality among adolescents. Although adolescent health risk behaviors such as alcohol use are amenable to behavioral intervention, few health information technology interventions have been integrated into adolescent care. The objective of this research is to design, implement, and investigate INSPIRE, a self-adaptive personalized behavior change system for adolescent preventive health with a focus on risky behavior and an emphasis on alcohol use. INSPIRE is being developed by a transdisciplinary team of psychologists, health services researchers, and computer scientists. With linkages through primary care, adolescents will interact with INSPIRE outside the clinic to develop an experiential understanding of the dynamics and consequences of their substance use decisions. The first phase of this research has been the design and initial development of the narrative centered behavior change environment.}, number={2}, journal={Journal of Adolescent Health}, publisher={Elsevier BV}, author={Ozer, Elizabeth M. and Jasik, Carolyn B. and Tebb, Kathleen P. and Erenrich, Rebecca K. and Berna, Mark and Rowe, Jonathan P. and Mott, Bradford W. and Lester, James C.}, year={2016}, month={Feb}, pages={S70} }
 @article{wiggins_grafsgaard_boyer_wiebe_lester_2016, title={Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science}, volume={27}, ISSN={1560-4292 1560-4306}, url={http://dx.doi.org/10.1007/s40593-015-0091-7}, DOI={10.1007/s40593-015-0091-7}, number={1}, journal={International Journal of Artificial Intelligence in Education}, publisher={Springer Science and Business Media LLC}, author={Wiggins, Joseph B. and Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2016}, month={Feb}, pages={130–153} }
 @article{shelton_smith_wiebe_behrle_sirkin_lester_2016, title={Drawing and Writing in Digital Science Notebooks: Sources of Formative Assessment Data}, volume={25}, ISSN={1059-0145 1573-1839}, url={http://dx.doi.org/10.1007/S10956-016-9607-7}, DOI={10.1007/S10956-016-9607-7}, number={3}, journal={Journal of Science Education and Technology}, publisher={Springer Science and Business Media LLC}, author={Shelton, Angi and Smith, Andrew and Wiebe, Eric and Behrle, Courtney and Sirkin, Ruth and Lester, James}, year={2016}, month={Feb}, pages={474–488} }
 @article{johnson_lester_2016, title={Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later}, volume={26}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-015-0065-9}, abstractNote={Johnson et al. (International Journal of Artificial Intelligence in Education, 11, 47–78, 2000) introduced and surveyed a new paradigm for interactive learning environments: animated pedagogical agents. The article argued for combining animated interface agent technologies with intelligent learning environments, yielding intelligent systems that can interact with learners in natural, human-like ways to achieve better learning outcomes. We outlined a variety of possible uses for pedagogical agents. But we offered only preliminary evidence that they improve learning, leaving that to future research and development. Twenty years have elapsed since work began on animated pedagogical agents. This article re-examines the concepts and predictions in the 2000 article in the context of the current state of the field. Some of the ideas in the paper have become well established and widely adopted, especially in game-based learning environments. Others are only now being realized, thanks to advances in immersive interfaces and robotics that enable rich face-to-face interaction between learners and agents. Research has confirmed that pedagogical agents can be beneficial, but not equally for all learning problems, applications, and learner populations. Although there is a growing body of research findings about pedagogical agents, many questions remain and much work remains to be done.}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Johnson, W. Lewis and Lester, James C.}, year={2016}, month={Mar}, pages={25–36} }
 @article{spires_lester_2016, title={Game-based learning: creating a multidisciplinary community of inquiry}, volume={24}, ISSN={["2054-1708"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84958580921&partnerID=MN8TOARS}, DOI={10.1108/oth-08-2015-0052}, abstractNote={Purpose – The purpose of this paper is to describe how the authors created a community of inquiry for game design with Crystal Island, report research results from a school pilot and analyze lessons learned. Using a community of inquiry approach, the authors created participatory structures for design and communication among the university team (i.e. computer science, literacy and science education, educational psychology and art design), elementary teachers and elementary students who were involved with Crystal Island. Design/methodology/approach – As part of the design process and in the attempt to create a community of inquiry, the authors conducted ongoing sessions with the teachers and students (N = 800), or what the authors refer to as design charettes. The design charettes included forming a lead teacher cadre and conducting game-based learning teacher institutes. These sessions led to a mixed methods school pilot study. Findings – Results of the classroom pilot study suggested that game-based lear...}, number={1}, journal={ON THE HORIZON}, author={Spires, Hiller A. and Lester, James C.}, year={2016}, pages={88–93} }
 @inbook{smith_aksit_min_wiebe_mott_lester_2016, place={Cham, Switzerland}, title={Integrating Real-Time Drawing and Writing Diagnostic Models: An Evidence-Centered Design Framework for Multimodal Science Assessment}, volume={9684}, ISBN={9783319395821 9783319395838}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-39583-8_16}, DOI={10.1007/978-3-319-39583-8_16}, abstractNote={Interactively modeling science phenomena enables students to develop rich conceptual understanding of science. While this understanding is often assessed through summative, multiple-choice instruments, science notebooks have been used extensively in elementary and secondary grades as a mechanism to promote and reveal reflection through both drawing and writing. Although each modality has been studied individually, obtaining a comprehensive view of a student’s conceptual understanding requires analyses of knowledge represented across both modalities. Evidence-centered design (ECD) provides a framework for diagnostic measurement of data collected from student interactions with complex learning environments. This work utilizes ECD to analyze a corpus of elementary student writings and drawings collected with a digital science notebook. First, a competency model representing the core concepts of each exercise, as well as the curricular unit as a whole, was constructed. Then, evidence models were created to map between student written and drawn artifacts and the shared competency model. Finally, the scores obtained using the evidence models were used to train a deep-learning based model for automated writing assessment, as well as to develop an automated drawing assessment model using topological abstraction. The findings reveal that ECD provides an expressive unified framework for multimodal assessment of science learning with accurate predictions of student learning.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer International Publishing}, author={Smith, Andy and Aksit, Osman and Min, Wookhee and Wiebe, Eric and Mott, Bradford W. and Lester, James C.}, editor={Micarelli, A. and Stamper, J. and Panourgia, K.Editors}, year={2016}, pages={165–175} }
 @inproceedings{smith_aksit_min_wiebe_mott_lester_2016, title={Integrating real-time drawing and writing diagnostic models: An evidence-centered design framework for multimodal science assessment}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Smith, A. and Aksit, O. and Min, W. and Wiebe, E. and Mott, B. W. and Lester, J. C.}, year={2016}, pages={165–175} }
 @article{vail_grafsgaard_boyer_wiebe_lester_2016, title={Predicting Learning from Student Affective Response to Tutor Questions}, volume={9684}, ISBN={["978-3-319-39582-1"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-39583-8_15}, abstractNote={Modeling student learning during tutorial interaction is a central problem in intelligent tutoring systems. While many modeling techniques have been developed to address this problem, most of them focus on cognitive models in conjunction with often-complex domain models. This paper presents an analysis suggesting that observing students’ multimodal behaviors may provide deep insight into student learning at critical moments in a tutorial session. In particular, this work examines student facial expression, electrodermal activity, posture, and gesture immediately following inference questions posed by human tutors. The findings show that for human-human task-oriented tutorial dialogue, facial expression and skin conductance response following tutor inference questions are highly predictive of student learning gains. These findings suggest that with multimodal behavior data, intelligent tutoring systems can make more informed adaptive decisions to support students effectively.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2016}, author={Vail, Alexandria K. and Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2016}, pages={154–164} }
 @article{tebb_erenrich_jasik_berna_lester_ozer_2016, title={Use of Theory in Computer-Based Interventions to Reduce Alcohol Use Among Adolescents and Young Adults}, volume={58}, ISSN={1054-139X}, url={http://dx.doi.org/10.1016/J.JADOHEALTH.2015.10.151}, DOI={10.1016/J.JADOHEALTH.2015.10.151}, abstractNote={Alcohol use and binge drinking among adolescents and young adults remain frequent causes of preventable injuries, disease and death. There has been growing attention to computer-based modes of intervention delivery to prevent/reduce alcohol use. Over the last decade, five literature reviews have examined the nascent field of digital interventions for alcohol use prevention targeting adolescents and young adults. Research suggests that health interventions grounded in established theory are more effective than those with no theoretical basis; however, there has been relatively little attention to the application of theory in CBIs designed to address alcohol use among adolescents and young adults. This study reviewed CBIs, targeting alcohol use among adolescents and young adults to examine the extent to which they use theories of behavior change in their development and evaluations. A secondary goal was to provide an update of CBIs addressing alcohol use among youth in order to expand our understanding of current interventions and their effectiveness.}, number={2}, journal={Journal of Adolescent Health}, publisher={Elsevier BV}, author={Tebb, Kathleen P. and Erenrich, Rebecca K. and Jasik, Carolyn B. and Berna, Mark and Lester, James C. and Ozer, Elizabeth M.}, year={2016}, month={Feb}, pages={S69–S70} }
 @article{tebb_erenrich_jasik_berna_lester_ozer_2016, title={Use of theory in computer-based interventions to reduce alcohol use among adolescents and young adults: a systematic review}, volume={16}, ISSN={["1471-2458"]}, DOI={10.1186/s12889-016-3183-x}, abstractNote={Alcohol use and binge drinking among adolescents and young adults remain frequent causes of preventable injuries, disease, and death, and there has been growing attention to computer-based modes of intervention delivery to prevent/reduce alcohol use. Research suggests that health interventions grounded in established theory are more effective than those with no theoretical basis. The goal of this study was to conduct a literature review of computer-based interventions (CBIs) designed to address alcohol use among adolescents and young adults (aged 12–21 years) and examine the extent to which CBIs use theories of behavior change in their development and evaluations. This study also provides an update on extant CBIs addressing alcohol use among youth and their effectiveness. Between November and December of 2014, a literature review of CBIs aimed at preventing or reducing alcohol in PsychINFO, PubMed, and Google Scholar was conducted. The use of theory in each CBI was examined using a modified version of the classification system developed by Painter et al. (Ann Behav Med 35:358–362, 2008). The search yielded 600 unique articles, 500 were excluded because they did not meet the inclusion criteria. The 100 remaining articles were retained for analyses. Many articles were written about a single intervention; thus, the search revealed a total of 42 unique CBIs. In examining the use of theory, 22 CBIs (52 %) explicitly named one or more theoretical frameworks. Primary theories mentioned were social cognitive theory, transtheoretical model, theory of planned behavior and reasoned action, and health belief model. Less than half (48 %), did not use theory, but mentioned either use of a theoretical construct (such as self-efficacy) or an intervention technique (e.g., manipulating social norms). Only a few articles provided detailed information about how the theory was applied to the CBI; the vast majority included little to no information. Given the importance of theory in guiding interventions, greater emphasis on the selection and application of theory is needed. The classification system used in this review offers a guiding framework for reporting how theory based principles can be applied to computer based interventions.}, journal={BMC PUBLIC HEALTH}, author={Tebb, Kathleen P. and Erenrich, Rebecca K. and Jasik, Carolyn Bradner and Berna, Mark S. and Lester, James C. and Ozer, Elizabeth M.}, year={2016}, month={Jun} }
 @article{taub_mudrick_azevedo_millar_rowe_lester_2016, title={Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with CRYSTAL ISLAND}, volume={9684}, ISBN={["978-3-319-39582-1"]}, ISSN={["0302-9743"]}, DOI={10.1007/978-3-319-39583-8_24}, abstractNote={Studies investigating the effectiveness of game-based learning environments (GBLEs) have reported the effectiveness of these environments on learning and retention. However, there is limited research on using eye-tracking data to investigate metacognitive monitoring with GBLEs. We report on a study that investigated how college students’ eye tracking behavior (n = 25) predicted performance on embedded assessments within the Crystal Island GBLE. Results revealed that the number of books, proportion of fixations on book and article content, and proportion of fixations on concept matrices—embedded assessments associated with each in-game book and article—significantly predicted the number of concept matrix attempts. These findings suggest that participants strategized when reading book and article content and completing assessments, which led to better performance. Implications for designing adaptive GBLEs include adapting to individual student needs based on eye-tracking behavior in order to foster efficient completion of in-game embedded assessments.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2016}, author={Taub, Michelle and Mudrick, Nicholas V. and Azevedo, Roger and Millar, Garrett C. and Rowe, Jonathan and Lester, James}, year={2016}, pages={240–246} }
 @article{min_frankosky_mott_rowe_wiebe_boyer_lester_2015, title={DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments}, volume={9112}, ISBN={["978-3-319-19772-2"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84949009361&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-19773-9_28}, abstractNote={A distinctive feature of intelligent game-based learning environments is their capacity for enabling stealth assessment. Stealth assessments gather information about student competencies in a manner that is invisible, and enable drawing valid inferences about student knowledge. We present a framework for stealth assessment that leverages deep learning, a family of machine learning methods that utilize deep artificial neural networks, to infer student competencies in a game-based learning environment for middle grade computational thinking, Engage. Students’ interaction data, collected during a classroom study with Engage, as well as prior knowledge scores, are utilized to train deep networks for predicting students’ post-test performance. Results indicate deep networks that are pre-trained using stacked denoising autoencoders achieve high predictive accuracy, significantly outperforming standard classification techniques such as support vector machines and naïve Bayes. The findings suggest that deep learning shows considerable promise for automatically inducing stealth assessment models for intelligent game-based learning environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015}, author={Min, Wookhee and Frankosky, Megan H. and Mott, Bradford W. and Rowe, Jonathan P. and Wiebe, Eric and Boyer, Kristy Elizabeth and Lester, James C.}, year={2015}, pages={277–286} }
 @inbook{smith_min_mott_lester_2015, title={Diagrammatic Student Models: Modeling Student Drawing Performance with Deep Learning}, volume={9146}, ISBN={9783319202662 9783319202679}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-20267-9_18}, DOI={10.1007/978-3-319-20267-9_18}, abstractNote={Recent years have seen a growing interest in the role that student drawing can play in learning. Because drawing has been shown to contribute to students’ learning and increase their engagement, developing student models to dynamically support drawing holds significant promise. To this end, we introduce diagrammatic student models, which reason about students’ drawing trajectories to generate a series of predictions about their conceptual knowledge based on their evolving sketches. The diagrammatic student modeling framework utilizes deep learning, a family of machine learning methods based on a deep neural network architecture, to reason about sequences of student drawing actions encoded with temporal and topological features. An evaluation of the deep-learning-based diagrammatic student models suggests that it can predict student performance more accurately and earlier than competitive baseline approaches.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Smith, Andy and Min, Wookhee and Mott, Bradford W. and Lester, James C.}, year={2015}, pages={216–227} }
 @inbook{rowe_lester_2015, title={Improving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework}, ISBN={9783319197722 9783319197739}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-19773-9_42}, DOI={10.1007/978-3-319-19773-9_42}, abstractNote={Narrative-centered learning environments comprise a class of game-based learning environments that embed problem solving in interactive stories. A key challenge posed by narrative-centered learning is dynamically tailoring story events to enhance student learning. In this paper, we investigate the impact of a data-driven tutorial planner on students’ learning processes in a narrative-centered learning environment, Crystal Island. We induce the tutorial planner by employing modular reinforcement learning, a multi-goal extension of classical reinforcement learning. To train the planner, we collected a corpus from 453 middle school students who used Crystal Island in their classrooms. Afterward, we investigated the induced planner’s impact in a follow-up experiment with another 75 students. The study revealed that the induced planner improved students’ problem-solving processes—including hypothesis testing and information gathering behaviors—compared to a control condition, suggesting that modular reinforcement learning is an effective approach for tutorial planning in narrative-centered learning environments.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Rowe, Jonathan P. and Lester, James C.}, year={2015}, pages={419–428} }
 @inproceedings{buffum_frankosky_boyer_wiebe_mott_lester_2015, title={Leveraging collaboration to improve gender equity in a game-based learning environment for middle school computer science}, DOI={10.1109/respect.2015.7296496}, abstractNote={Game-based learning environments can deliver robust learning gains and also have a unique capacity to engage students. Yet they can unintentionally disadvantage students with less prior gaming experience. This is especially concerning in computer science education, as certain underrepresented groups (such as female students) may on average have less prior experience with games. This paper presents evidence that a collaborative gameplay approach can successfully address this problem at the middle school level. In an iterative, designed-based research study, we first used an experimental pilot study to investigate the nature of collaboration in the Engage game-based learning environment, and then deployed Engage in a full classroom study to measure its effectiveness at serving all students. In earlier phases of the intervention, male students outpaced their female peers in learning gains. However, female students caught up during a multi-week classroom implementation. These findings provide evidence that a collaborative gameplay approach may, over time, compensate for gender differences in experience and lead to equitable learning experiences within game-based learning environments for computer science education.}, booktitle={2015 Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT)}, author={Buffum, P. S. and Frankosky, M. and Boyer, K. E. and Wiebe, Eric and Mott, B. and Lester, J.}, year={2015} }
 @article{buffum_boyer_wiebe_mott_lester_2015, title={Mind the Gap: Improving Gender Equity in Game-Based Learning Environments with Learning Companions}, volume={9112}, ISBN={["978-3-319-19772-2"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-19773-9_7}, abstractNote={Game-based learning environments hold great promise for engaging learners. Yet game mechanics can initially pose barriers for students with less prior gaming experience. This paper examines game-based learning for a population of middle school learners in the US, where female students tend to have less gaming experience than male students. In a pilot study with an early version of Engage, a game-based learning environment for middle school computer science education, female students reported higher initial frustration. To address this critical issue, we developed a prototype learning companion designed specifically to reduce frustration through the telling of autobiographical stories. In a pilot study of two 7th grade classrooms, female students responded especially positively to the learning companion, eliminating the gender gap in reported frustration. The results suggest that introducing learning companions can directly contribute to making the benefits of game-based learning equitable for all learners.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015}, author={Buffum, Philip Sheridan and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Mott, Bradford W. and Lester, James C.}, year={2015}, pages={64–73} }
 @inbook{grafsgaard_lee_mott_boyer_lester_2015, title={Modeling Self-Efficacy Across Age Groups with Automatically Tracked Facial Expression}, ISBN={9783319197722 9783319197739}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-19773-9_67}, DOI={10.1007/978-3-319-19773-9_67}, abstractNote={Affect plays a central role in learning. Students’ facial expressions are key indicators of affective states and recent work has increasingly used automated facial expression tracking technologies as a method of affect detection. However, there has not been an investigation of facial expressions compared across age groups. The present study collected facial expressions of college and middle school students in the Crystal Island game-based learning environment. Facial expressions were tracked using the Computer Expression Recognition Toolbox and models of self-efficacy for each age group highlighted differences in facial expressions. Age-specific findings such as these will inform the development of enriched affect models for broadening populations of learners using affect-sensitive learning environments.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Grafsgaard, Joseph F. and Lee, Seung Y. and Mott, Bradford W. and Boyer, Kristy Elizabeth and Lester, James C.}, year={2015}, pages={582–585} }
 @inbook{vail_boyer_wiebe_lester_2015, title={The Mars and Venus Effect: The Influence of User Gender on the Effectiveness of Adaptive Task Support}, ISBN={9783319202662 9783319202679}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-20267-9_22}, DOI={10.1007/978-3-319-20267-9_22}, abstractNote={Providing adaptive support to users engaged in learning tasks is the central focus of intelligent tutoring systems. There is evidence that female and male users may benefit differently from adaptive support, yet it is not understood how to most effectively adapt task support to gender. This paper reports on a study with four versions of an intelligent tutoring system for introductory computer programming offering different levels of cognitive (conceptual and problem-solving) and affective (motivational and engagement) support. The results show that female users reported significantly more engagement and less frustration with the affective support system than with other versions. In a human tutorial dialogue condition used for comparison, a consistent difference was observed between females and males. These results suggest the presence of the Mars and Venus Effect, a systematic difference in how female and male users benefit from cognitive and affective adaptive support. The findings point toward design principles to guide the development of gender-adaptive intelligent tutoring systems.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Vail, Alexandria Katarina and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2015}, pages={265–276} }
 @inproceedings{vail_boyer_wiebe_lester_2015, title={The Mars and Venus effect: The influence of user gender on the effectiveness of adaptive task support}, volume={9146}, booktitle={User modeling, adaptation and personalization}, author={Vail, A. K. and Boyer, K. E. and Wiebe, E. N. and Lester, J. C.}, year={2015}, pages={265–276} }
 @article{leeman-munk_smith_mott_wiebe_lester_2015, title={Two Modes Are Better Than One: A Multimodal Assessment Framework Integrating Student Writing and Drawing}, volume={9112}, ISBN={["978-3-319-19772-2"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84948972893&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-19773-9_21}, abstractNote={We are beginning to see the emergence of advanced automated assessment techniques that evaluate expressive student artifacts such as free-form written responses and sketches. These approaches have largely operated individually, each considering only a single mode. We hypothesize that there are synergies to be leveraged in multimodal assessments that can integrate multiple modalities of student responses to create a more complete and accurate picture of a student’s knowledge. In this paper, we introduce a novel multimodal assessment framework that integrates two techniques for automatically analyzing student artifacts: a deep learning-based model for assessing student writing, and a topology-based model for assessing student drawing. An evaluation of the framework with elementary students’ writing and drawing assessments demonstrate that 1) each of the framework’s two modalities provides an independent and complementary measure of student science learning, and 2) together, the multimodal framework significantly outperforms either uni-modal approach individually, demonstrating the potential synergistic benefits of multimodal assessment.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015}, author={Leeman-Munk, Samuel and Smith, Andy and Mott, Bradford and Wiebe, Eric and Lester, James}, year={2015}, pages={205–215} }
 @article{lee_rowe_mott_lester_2014, title={A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative}, volume={6}, ISSN={["1943-0698"]}, DOI={10.1109/tciaig.2013.2292010}, abstractNote={Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players and support learning. A key challenge posed by interactive narrative is devising effective director agent models that dynamically sequence story events according to players' actions and needs. In this paper, we describe a supervised machine-learning framework to model director agent strategies in an educational interactive narrative Crystal Island. Findings from two studies with human participants are reported. The first study utilized a Wizard-of-Oz paradigm where human “wizards” directed participants through Crystal Island's mystery storyline by dynamically controlling narrative events in the game environment. Interaction logs yielded training data for machine learning the conditional probabilities of a dynamic Bayesian network (DBN) model of the human wizards' directorial actions. Results indicate that the DBN model achieved significantly higher precision and recall than naive Bayes and bigram model techniques. In the second study, the DBN director agent model was incorporated into the runtime version of Crystal Island, and its impact on students' narrative-centered learning experiences was investigated. Results indicate that machine-learning director agent strategies from human demonstrations yield models that positively shape players' narrative-centered learning and problem-solving experiences.}, number={2}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES}, author={Lee, Seung Y. and Rowe, Jonathan P. and Mott, Bradford W. and Lester, James C.}, year={2014}, month={Jun}, pages={203–215} }
 @article{sabourin_lester_2014, title={Affect and Engagement in Game-Based Learning Environments}, volume={5}, ISSN={["1949-3045"]}, DOI={10.1109/t-affc.2013.27}, abstractNote={The link between affect and student learning has been the subject of increasing attention in recent years. Affective states such as flow and curiosity tend to have positive correlations with learning while negative states such as boredom and frustration have the opposite effect. Student engagement and motivation have also been shown to be critical in improving learning gains with computer-based learning environments. Consequently, it is a design goal of many computer-based learning environments to encourage positive affect and engagement while students are learning. Game-based learning environments offer significant potential for increasing student engagement and motivation. However, it is unclear how affect and engagement interact with learning in game-based learning environments. This work presents an in-depth analysis of how these phenomena occur in the game-based learning environment, Crystal Island. The findings demonstrate that game-based learning environments can simultaneously support learning and promote positive affect and engagement.}, number={1}, journal={IEEE TRANSACTIONS ON AFFECTIVE COMPUTING}, author={Sabourin, Jennifer L. and Lester, James C.}, year={2014}, pages={45–56} }
 @article{lester_spires_nietfeld_minogue_mott_lobene_2014, title={Designing game-based learning environments for elementary science education: A narrative-centered learning perspective}, volume={264}, ISSN={["1872-6291"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84894486887&partnerID=MN8TOARS}, DOI={10.1016/j.ins.2013.09.005}, abstractNote={Game-based learning environments hold significant promise for STEM education, yet they are enormously complex. Crystal Island: Uncharted Discovery, is a game-based learning environment designed for upper elementary science education that has been under development in our laboratory for the past four years. This article discusses curricular and narrative interaction design requirements, presents the design of the Crystal Island learning environment, and describes its evolution through a series of pilots and field tests. Additionally, a classroom integration study was conducted to initiate a shift towards ecological validity. Results indicated that Crystal Island produced significant learning gains on both science content and problem-solving measures. Importantly, gains were consistent for gender across studies. This finding is key in light of past studies that revealed disproportionate participation by boys within game-based learning environments.}, journal={INFORMATION SCIENCES}, author={Lester, James C. and Spires, Hiller A. and Nietfeld, John L. and Minogue, James and Mott, Bradford W. and Lobene, Eleni V.}, year={2014}, month={Apr}, pages={4–18} }
 @inproceedings{baikadi_rowe_mott_lester_2014, title={Generalizability of goal recognition models in narrative-centered learning environments}, volume={8538}, DOI={10.1007/978-3-319-08786-3_24}, abstractNote={Recent years have seen growing interest in automated goal recognition. In user-adaptive systems, goal recognition is the problem of recognizing a user’s goals by observing the actions the user performs. Models of goal recognition can support student learning in intelligent tutoring systems, enhance communication efficiency in dialogue systems, or dynamically adapt software to users’ interests. In this paper, we describe an approach to goal recognition that leverages Markov Logic Networks (MLNs)—a machine learning framework that combines probabilistic inference with first-order logical reasoning—to encode relations between problem-solving goals and discovery events, domain-specific representations of user progress in narrative-centered learning environments. We investigate the impact of discovery event representations on goal recognition accuracy and efficiency. We also investigate the generalizability of discovery event-based goal recognition models across two corpora from students interacting with two distinct narrative-centered learning environments. Empirical results indicate that discovery event-based models outperform previous state-of-the-art approaches on both corpora.}, booktitle={User modeling, adaptation, and personalization, umap 2014}, author={Baikadi, A. and Rowe, J. and Mott, B. and Lester, J.}, year={2014}, pages={278–289} }
 @book{min_mott_rowe_lester_2014, title={Leveraging semi-supervised learning to predict student problem-solving performance in narrative-centered learning environments}, volume={8474 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84958543350&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-07221-0_99}, abstractNote={This paper presents a semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments. Results suggest the approach often outperforms standard supervised learning methods.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Min, Wookhee and Mott, B.W. and Rowe, J.P. and Lester, J.C.}, year={2014}, pages={664–665} }
 @inproceedings{rowe_lobene_mott_lester_2014, title={Serious games go informal: a museum-centric perspective on intelligent game-based learning}, volume={8474}, DOI={10.1007/978-3-319-07221-0_51}, abstractNote={Intelligent game-based learning environments show considerable promise for creating effective and engaging learning experiences that are tailored to individuals. To date, much of the research on intelligent game-based learning environments has focused on formal education settings and training. However, intelligent game-based learning environments also offer significant potential for informal education settings, such as museums and science centers. In this paper, we describe Future Worlds, a prototype game-based learning environment for collaborative explorations of sustainability in science museums. We report findings from a study investigating the influence of individual differences on learning and engagement in Future Worlds. Results indicate that learners showed significant gains in sustainability knowledge as well as high levels of engagement. Boys were observed to actively engage with Future Worlds for significantly longer than girls, and young children engaged with the exhibit longer than older children. These findings support the promise of intelligent game-based learning environments that dynamically recognize and adapt to learners’ individual differences during museum learning.}, booktitle={Intelligent tutoring systems, its 2014}, author={Rowe, J. P. and Lobene, E. V. and Mott, B. W. and Lester, J. C.}, year={2014}, pages={410–415} }
 @inbook{mitchell_boyer_lester_2013, title={A Markov Decision Process Model of Tutorial Intervention in Task-Oriented Dialogue}, ISBN={9783642391118 9783642391125}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-39112-5_123}, DOI={10.1007/978-3-642-39112-5_123}, abstractNote={Designing dialogue systems that engage in rich tutorial dialogue has long been a goal of the intelligent tutoring systems community. A key challenge for these systems is determining when to intervene during student problem solving. Although intervention strategies have historically been hand-authored, utilizing machine learning to automatically acquire corpus-based intervention policies that maximize student learning holds great promise. To this end, this paper presents a Markov Decision Process (MDP) framework to learn an intervention policy capturing the most effective tutor turn-taking behaviors in a task-oriented learning environment with textual dialogue. The model and its learned policy highlight important design considerations, including maintaining tutor engagement during student problem solving and avoiding multiple consecutive interventions.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Mitchell, Christopher M. and Boyer, Kristy Elizabeth and Lester, James C.}, year={2013}, pages={828–831} }
 @article{grafsgaard_wiggins_boyer_wiebe_lester_2013, title={Automatically Recognizing Facial Indicators of Frustration: A Learning-Centric Analysis}, ISSN={["2156-8103"]}, DOI={10.1109/acii.2013.33}, abstractNote={Affective and cognitive processes form a rich substrate on which learning plays out. Affective states often influence progress on learning tasks, resulting in positive or negative cycles of affect that impact learning outcomes. Developing a detailed account of the occurrence and timing of cognitive-affective states during learning can inform the design of affective tutorial interventions. In order to advance understanding of learning-centered affect, this paper reports on a study to analyze a video corpus of computer-mediated human tutoring using an automated facial expression recognition tool that detects fine-grained facial movements. The results reveal three significant relationships between facial expression, frustration, and learning: (1) Action Unit 2 (outer brow raise) was negatively correlated with learning gain, (2) Action Unit 4 (brow lowering) was positively correlated with frustration, and (3) Action Unit 14 (mouth dimpling) was positively correlated with both frustration and learning gain. Additionally, early prediction models demonstrated that facial actions during the first five minutes were significantly predictive of frustration and learning at the end of the tutoring session. The results represent a step toward a deeper understanding of learning-centered affective states, which will form the foundation for data-driven design of affective tutoring systems.}, journal={2013 HUMAINE ASSOCIATION CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)}, author={Grafsgaard, Joseph F. and Wiggins, Joseph B. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2013}, pages={159–165} }
 @inbook{sabourin_mott_lester_2013, title={Discovering Behavior Patterns of Self-Regulated Learners in an Inquiry-Based Learning Environment}, ISBN={9783642391118 9783642391125}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-39112-5_22}, DOI={10.1007/978-3-642-39112-5_22}, abstractNote={Inquiry-based learning has been proposed as a natural and authentic way for students to engage with science. Inquiry-based learning environments typically require students to guide their own learning and inquiry processes as they gather data, make and test hypotheses and draw conclusions. Some students are highly self-regulated learners and are able to guide and monitor their own learning activities effectively. Unfortunately, many students lack these skills and are consequently less successful in open-ended, inquiry-based environments. This work examines differences in inquiry behavior patterns in an open-ended, game-based learning environment, Crystal Island. Differential sequence mining is used to identify meaningful behavior patterns utilized by Low, Medium, and High self-regulated learners. Results indicate that self-regulated learners engage in more effective problem solving behaviors and demonstrate different patterns of use of the provided cognitive tools. The identified patterns help provide further insight into the role of SRL in inquiry-based learning and inform future approaches for scaffolding.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Mott, Bradford and Lester, James}, year={2013}, pages={209–218} }
 @inbook{grafsgaard_wiggins_boyer_wiebe_lester_2013, title={Embodied Affect in Tutorial Dialogue: Student Gesture and Posture}, ISBN={9783642391118 9783642391125}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-39112-5_1}, DOI={10.1007/978-3-642-39112-5_1}, abstractNote={Recent years have seen a growing recognition of the central role of affect and motivation in learning. In particular, nonverbal behaviors such as posture and gesture provide key channels signaling affective and motivational states. Developing a clear understanding of these mechanisms will inform the development of personalized learning environments that promote successful affective and motivational outcomes. This paper investigates posture and gesture in computer-mediated tutorial dialogue using automated techniques to track posture and hand-to-face gestures. Annotated dialogue transcripts were analyzed to identify the relationships between student posture, student gesture, and tutor and student dialogue. The results indicate that posture and hand-to-face gestures are significantly associated with particular tutorial dialogue moves. Additionally, two-hands-to-face gestures occurred significantly more frequently among students with low self-efficacy. The results shed light on the cognitive-affective mechanisms that underlie these nonverbal behaviors. Collectively, the findings provide insight into the interdependencies among tutorial dialogue, posture, and gesture, revealing a new avenue for automated tracking of embodied affect during learning.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Grafsgaard, Joseph F. and Wiggins, Joseph B. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2013}, pages={1–10} }
 @inbook{min_rowe_mott_lester_2013, title={Personalizing Embedded Assessment Sequences in Narrative-Centered Learning Environments: A Collaborative Filtering Approach}, ISBN={9783642391118 9783642391125}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-39112-5_38}, DOI={10.1007/978-3-642-39112-5_38}, abstractNote={A key challenge posed by narrative-centered learning environments is dynamically tailoring story events to individual students. This paper investigates techniques for sequencing story-centric embedded assessments—a particular type of story event that simultaneously evaluates a student’s knowledge and advances an interactive narrative’s plot—in narrative-centered learning environments. We present an approach for personalizing embedded assessment sequences that is based on collaborative filtering. We examine personalized event sequencing in an edition of the Crystal Island narrative-centered learning environment for literacy education. Using data from a multi-week classroom study with 850 students, we compare two model-based collaborative filtering methods, including probabilistic principal component analysis (PPCA) and non-negative matrix factorization (NMF), to a memory-based baseline model, k-nearest neighbor. Results suggest that PPCA provides the most accurate predictions on average, but NMF provides a better balance between accuracy and run-time efficiency for predicting student performance on story-centric embedded assessment sequences.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Min, Wookhee and Rowe, Jonathan P. and Mott, Bradford W. and Lester, James C.}, year={2013}, pages={369–378} }
 @article{lester_ha_lee_mott_rowe_sabourin_2013, title={Serious Games Get Smart: Intelligent Game-Based Learning Environments}, volume={34}, ISSN={["0738-4602"]}, DOI={10.1609/aimag.v34i4.2488}, abstractNote={Intelligent game‐based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the Crystal Island intelligent game‐based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting Crystal Island, the principal technical problems of intelligent game‐based learning environments are discussed: narrative‐centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the Crystal Island learning environment}, number={4}, journal={AI MAGAZINE}, author={Lester, James C. and Ha, Eun Y. and Lee, Seung Y. and Mott, Bradford W. and Rowe, Jonathan P. and Sabourin, Jennifer L.}, year={2013}, pages={31–45} }
 @article{sabourin_shores_mott_lester_2013, title={Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments}, volume={23}, ISSN={1560-4292 1560-4306}, url={http://dx.doi.org/10.1007/S40593-013-0004-6}, DOI={10.1007/S40593-013-0004-6}, abstractNote={Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students’ success in computer-based learning environments. Consequently, understanding students’ self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation into self-regulated learning in a game-based learning environment. Evidence of goal setting and monitoring behaviors is examined through students’ text-based responses to update their ‘status’ in an in-game social network. Students are then classified into SRL-use categories. This article describes the methodology used to classify students and discusses analyses demonstrating the learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these classes early in students’ interaction are presented.}, number={1-4}, journal={International Journal of Artificial Intelligence in Education}, publisher={Springer Science and Business Media LLC}, author={Sabourin, Jennifer L. and Shores, Lucy R. and Mott, Bradford W. and Lester, James C.}, year={2013}, month={Oct}, pages={94–114} }
 @inbook{sabourin_mott_lester_2013, title={Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning}, ISBN={9783642388439 9783642388446}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-38844-6_19}, DOI={10.1007/978-3-642-38844-6_19}, abstractNote={Student engagement and motivation during learning activities is tied to better learning behaviors and outcomes and has prompted the development of learner-guided environments. These systems attempt to personalize learning by allowing students to select their own tasks and activities. However, recent evidence suggests that not all students are equally capable of guiding their own learning. Some students are highly self-regulated learners and are able to select learning goals, identify appropriate tasks and activities to achieve these goals and monitor their progress resulting in improved learning and motivational benefits over traditional learning tasks. Students who lack these skills are markedly less successful in self-guided learning environments and require additional scaffolding to be able to navigate them successfully. Prior work has examined these phenomena within the learner-guided environment, Crystal Island, and identified the need for early prediction of students’ self-regulated learning abilities. This work builds upon these findings and presents a dynamic Bayesian approach that significantly improves the classification accuracy of student self-regulated learning skills.}, booktitle={User Modeling, Adaptation, and Personalization}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Mott, Bradford and Lester, James}, year={2013}, pages={228–241} }
 @article{meluso_zheng_spires_lester_2012, title={Enhancing 5th graders’ science content knowledge and self-efficacy through game-based learning}, volume={59}, ISSN={0360-1315}, url={http://dx.doi.org/10.1016/j.compedu.2011.12.019}, DOI={10.1016/j.compedu.2011.12.019}, abstractNote={Many argue that games can positively impact learning by providing an intrinsically motivating and engaging learning environment for students in ways that traditional school cannot. Recent research demonstrates that games have the potential to impact student learning in STEM content areas and that collaborative gameplay may be of particular importance for learning gains. This study investigated the effects of collaborative and single game player conditions on science content learning and science self-efficacy. Results indicated that there were no differences between the two playing conditions; however, when conditions were collapsed, science content learning and self-efficacy significantly increased. Future research should focus on the composition of collaboration interaction among game players to assess what types of collaborative tasks may yield positive learning gains.}, number={2}, journal={Computers & Education}, publisher={Elsevier BV}, author={Meluso, Angela and Zheng, Meixun and Spires, Hiller A. and Lester, James}, year={2012}, month={Sep}, pages={497–504} }
 @inbook{sabourin_rowe_mott_lester_2012, title={Exploring Inquiry-Based Problem-Solving Strategies in Game-Based Learning Environments}, ISBN={9783642309496 9783642309502}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-30950-2_60}, DOI={10.1007/978-3-642-30950-2_60}, abstractNote={Guided inquiry-based learning has been proposed as a promising approach to science education. Students are encouraged to gather information, use this information to iteratively formulate and test hypotheses, draw conclusions, and report their findings. However, students may not automatically follow this prescribed sequence of steps in open-ended learning environments. This paper examines the role of inquiry behaviors in an open-ended, game-based learning environment for middle grade microbiology. Results indicate that students’ quantity of information-gathering behaviors has a greater impact on content learning gains than adherence to a particular sequence of problem-solving steps. We also observe that information gathering prior to hypothesis generation is correlated with improved initial hypotheses and problem-solving efficiency.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Rowe, Jonathan and Mott, Bradford W. and Lester, James C.}, year={2012}, pages={470–475} }
 @inproceedings{grafsgaard_fulton_boyer_wiebe_lester_2012, title={Multimodal analysis of the implicit affective channel in computer-mediated textual communication}, DOI={10.1145/2388676.2388708}, abstractNote={Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.}, booktitle={ICMI '12: Proceedings of the ACM International Conference on Multimodal Interaction}, author={Grafsgaard, J. F. and Fulton, R. M. and Boyer, K. E. and Wiebe, E. N. and Lester, J. C.}, year={2012}, pages={145–152} }
 @inbook{sabourin_shores_mott_lester_2012, title={Predicting Student Self-regulation Strategies in Game-Based Learning Environments}, ISBN={9783642309496 9783642309502}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-30950-2_19}, DOI={10.1007/978-3-642-30950-2_19}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Shores, Lucy R. and Mott, Bradford W. and Lester, James C.}, year={2012}, pages={141–150} }
 @inbook{lee_mott_lester_2012, title={Real-Time Narrative-Centered Tutorial Planning for Story-Based Learning}, ISBN={9783642309496 9783642309502}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-30950-2_61}, DOI={10.1007/978-3-642-30950-2_61}, abstractNote={Interactive story-based learning environments offer significant potential for crafting narrative tutorial guidance to create pedagogically effective learning experiences that are tailored to individual students. This paper reports on an empirical evaluation of machine-learned models of narrative-centered tutorial planning for story-based learning environments. We investigate differences in learning gains and in-game performance during student interactions in a rich virtual storyworld. One hundred and eighty-three middle school students participated in the study, which had three conditions: Minimal Guidance, Intermediate Guidance, and Full Guidance. Results reveal statistically significant differences in learning and in-game problem-solving effectiveness between students who received minimal guidance and students who received full guidance. Students in the full guidance condition tended to demonstrate higher learning outcomes and problem-solving efficiency. The findings suggest that machine-learned models of narrative-centered tutorial planning can improve learning outcomes and in-game efficiency.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Lee, Seung Y. and Mott, Bradford W. and Lester, James C.}, year={2012}, pages={476–481} }
 @inbook{shores_hoffmann_nietfeld_lester_2012, title={The Role of Sub-problems: Supporting Problem Solving in Narrative-Centered Learning Environments}, ISBN={9783642309496 9783642309502}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-30950-2_59}, DOI={10.1007/978-3-642-30950-2_59}, abstractNote={Narrative-centered learning environments provide an excellent platform for both content-knowledge and problem-solving skill acquisition, as these experiences require students to apply learned material while solving real-world problems. Solving complex problems in an open-ended environment can be a challenging endeavor for elementary students given limitations in their cognitive skills. A promising potential solution is providing students with explicit quests, or proximal goals of a larger, more complex problem-solving activity. Quests have the potential to scaffold the process by breaking down the problem into cognitively manageable units, providing useful, frequent feedback, and maintaining motivation and the novelty of the experience. The aim of this research was to investigate the role of quests as a means for supporting situational interest and content-knowledge acquisition during interactions with a narrative-centered learning environment. Of the 299 5th grade students who interacted with Crystal Island, a narrative-centered learning environment for science, it was found that students who completed more quests exhibited significant increases in content learning and had higher levels of situational interest. These preliminary findings suggest potential educational and motivational advantages for integrating quest-like sub-problems into the design of narrative-centered learning environments.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Shores, Lucy R. and Hoffmann, Kristin F. and Nietfeld, John L. and Lester, James C.}, year={2012}, pages={464–469} }
 @inbook{grafsgaard_boyer_lester_2012, title={Toward a Machine Learning Framework for Understanding Affective Tutorial Interaction}, ISBN={9783642309496 9783642309502}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-30950-2_7}, DOI={10.1007/978-3-642-30950-2_7}, abstractNote={Affect and cognition intertwine throughout human experience. Research into this interplay during learning has identified relevant cognitive-affective states, but recognizing them poses significant challenges. Among multiple promising approaches for affect recognition, analyzing facial expression may be particularly informative. Descriptive computational models of facial expression and affect, such as those enabled by machine learning, aid our understanding of tutorial interactions. Hidden Markov modeling, in particular, is useful for encoding patterns in sequential data. This paper presents a descriptive hidden Markov model built upon facial expression data and tutorial dialogue within a task-oriented human-human tutoring corpus. The model reveals five frequently occurring patterns of affective tutorial interaction across text-based tutorial dialogue sessions. The results show that hidden Markov modeling holds potential for the semi-automated understanding of affective interaction, which may contribute to the development of affect-informed intelligent tutoring systems.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Lester, James C.}, year={2012}, pages={52–58} }
 @inbook{lester_2011, place={Berlin Heidelberg}, series={Lecture Notes in Computer Science}, title={Affect, Learning, and Delight}, ISBN={9783642245992 9783642246005}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-24600-5_2}, DOI={10.1007/978-3-642-24600-5_2}, abstractNote={Because of the growing recognition of the role that affect plays in learning, affective computing has become the subject of increasing attention in research on interactive learning environments. The intelligent tutoring systems community has begun actively exploring computational models of affect, and game-based learning environments present a significant opportunity for investigating student affect in interactive learning. One family of game-based learning environments, narrative-centered learning environments, offer a particularly compelling laboratory for investigating student affect. In narrative-centered environments, learning activities play out in dynamically generated interactive narratives and training scenarios. These afford significant opportunities for investigating computational models of student emotion. In this talk, we explore the role that affective computing can play in next-generation interactive learning environments, with a particular focus on affect recognition, affect understanding, and affect synthesis in game-based learning.}, booktitle={Affective Computing and Intelligent Interaction. ACII 2011}, publisher={Springer}, author={Lester, James C.}, editor={D'Mello, S. and Graesser, A. and Schuller, B. and Martin, J.C.Editors}, year={2011}, pages={2–2}, collection={Lecture Notes in Computer Science} }
 @inbook{lee_mott_lester_2011, title={Director Agent Intervention Strategies for Interactive Narrative Environments}, ISBN={9783642252884 9783642252891}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-25289-1_15}, DOI={10.1007/978-3-642-25289-1_15}, abstractNote={Interactive narrative environments offer significant potential for creating engaging narrative experiences. Increasingly, applications in education, training, and entertainment are leveraging narrative to create rich interactive experiences in virtual storyworlds. A key challenge posed by these environments is building an effective model of the intervention strategies of director agents that craft customized story experiences for users. Identifying factors that contribute to determining when the next director agent decision should occur is critically important in optimizing narrative experiences. In this work, a dynamic Bayesian network framework was designed to model director agent intervention strategies. To create empirically informed models of director agent intervention decisions, we conducted a Wizard-of-Oz (WOZ) data collection with an interactive narrative-centered learning environment. Using the collected data, dynamic Bayesian network and naïve Bayes models were learned and compared. The performance of the resulting models was evaluated with respect to classification accuracy and produced promising results.}, booktitle={Interactive Storytelling}, publisher={Springer Berlin Heidelberg}, author={Lee, Seung Y. and Mott, Bradford W. and Lester, James C.}, year={2011}, pages={140–151} }
 @inbook{shores_rowe_lester_2011, title={Early Prediction of Cognitive Tool Use in Narrative-Centered Learning Environments}, ISBN={9783642218682 9783642218699}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-21869-9_42}, DOI={10.1007/978-3-642-21869-9_42}, abstractNote={Narrative-centered learning environments introduce novel opportunities for supporting student problem solving and learning. By incorporating cognitive tools into plots and character roles, narrative-centered learning environments can promote self-regulated learning in a manner that is transparent to students. In order to adapt narrative plots to explicitly support effective cognitive tool-use, narrative-centered learning environments need to be able to make early predictions about how effectively students will utilize learning resources. This paper presents results from an investigation into machine-learned models for making early predictions about students’ use of a specific cognitive tool in the Crystal Island learning environment. Multiple classification models are compared and discussed. Findings suggest that support vector machine and naïve Bayes models offer considerable promise for generating useful predictive models of cognitive tool use in narrative-centered learning environments.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Shores, Lucy R. and Rowe, Jonathan P. and Lester, James C.}, year={2011}, pages={320–327} }
 @inbook{sabourin_mott_lester_2011, title={Generalizing Models of Student Affect in Game-Based Learning Environments}, ISBN={9783642245701 9783642245718}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-24571-8_73}, DOI={10.1007/978-3-642-24571-8_73}, abstractNote={Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are designed in ways that limit their ability to be deployed to a large audience of students by using expensive sensors or subject-dependent machine learning techniques. This paper presents work that investigates empirically derived Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, Crystal Island. These models are then tested on data from a second identical study involving 140 students to examine issues of generalizability of learned predictive models of student affect. The findings suggest that predictive models of affect that are learned from empirical data may have significant dependencies on the populations on which they are trained, even when the populations themselves are very similar.}, booktitle={Affective Computing and Intelligent Interaction}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Mott, Bradford and Lester, James C.}, year={2011}, pages={588–597} }
 @inbook{grafsgaard_boyer_phillips_lester_2011, title={Modeling Confusion: Facial Expression, Task, and Discourse in Task-Oriented Tutorial Dialogue}, ISBN={9783642218682 9783642218699}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-21869-9_15}, DOI={10.1007/978-3-642-21869-9_15}, abstractNote={Recent years have seen a growing recognition of the importance of affect in learning. Efforts are being undertaken to enable intelligent tutoring systems to recognize and respond to learner emotion, but the field has not yet seen the emergence of a fully contextualized model of learner affect. This paper reports on a study of learner affect through an analysis of facial expression in human task-oriented tutorial dialogue. It extends prior work through in-depth analyses of a highly informative facial action unit and its interdependencies with dialogue utterances and task structure. The results demonstrate some ways in which learner facial expressions are dependent on both dialogue and task context. The findings also hold design implications for affect recognition and tutorial strategy selection within tutorial dialogue systems.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Phillips, Robert and Lester, James C.}, year={2011}, pages={98–105} }
 @inbook{sabourin_mott_lester_2011, title={Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks}, ISBN={9783642245992 9783642246005}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-24600-5_32}, DOI={10.1007/978-3-642-24600-5_32}, abstractNote={Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, Crystal Island. Results indicate the benefits of using theoretical models of learner emotions to inform predictive models. The most successful model, a dynamic Bayesian network, also highlights the importance of temporal information in predicting learner emotions. This work demonstrates the benefits of basing predictive models of learner emotions on theoretical foundations and has implications for how these models may be used to validate theoretical models of emotion.}, booktitle={Affective Computing and Intelligent Interaction}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Mott, Bradford and Lester, James C.}, year={2011}, pages={286–295} }
 @inbook{lee_mott_lester_2011, title={Modeling Narrative-Centered Tutorial Decision Making in Guided Discovery Learning}, ISBN={9783642218682 9783642218699}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-21869-9_23}, DOI={10.1007/978-3-642-21869-9_23}, abstractNote={Interactive narrative-centered learning environments offer significant potential for scaffolding guided discovery learning in rich virtual storyworlds while creating engaging and pedagogically effective experiences. Within these environments students actively participate in problem-solving activities. A significant challenge posed by narrative-centered learning environments is devising accurate models of narrative-centered tutorial decision making to craft customized story-based learning experiences for students. A promising approach is developing empirically driven models of narrative-centered tutorial decision-making. In this work, a dynamic Bayesian network has been designed to make narrative-centered tutorial decisions. The network parameters were learned from a corpus collected in a Wizard-of-Oz study in which narrative and tutorial planning activities were performed by humans. The performance of the resulting model was evaluated with respect to predictive accuracy and yields encouraging results.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Lee, Seung Y. and Mott, Bradford W. and Lester, James C.}, year={2011}, pages={163–170} }
 @inbook{grafsgaard_boyer_lester_2011, title={Predicting Facial Indicators of Confusion with Hidden Markov Models}, ISBN={9783642245992 9783642246005}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-24600-5_13}, DOI={10.1007/978-3-642-24600-5_13}, abstractNote={Affect plays a vital role in learning. During tutoring, particular affective states may benefit or detract from student learning. A key cognitive-affective state is confusion, which has been positively associated with effective learning. Although identifying episodes of confusion presents significant challenges, recent investigations have identified correlations between confusion and specific facial movements. This paper builds on those findings to create a predictive model of learner confusion during task-oriented human-human tutorial dialogue. The model leverages textual dialogue, task, and facial expression history to predict upcoming confusion within a hidden Markov modeling framework. Analysis of the model structure also reveals meaningful modes of interaction within the tutoring sessions. The results demonstrate that because of its predictive power and rich qualitative representation, the model holds promise for informing the design of affective-sensitive tutoring systems.}, booktitle={Affective Computing and Intelligent Interaction}, publisher={Springer Berlin Heidelberg}, author={Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Lester, James C.}, year={2011}, pages={97–106} }
 @inbook{sabourin_rowe_mott_lester_2011, title={When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments}, ISBN={9783642218682 9783642218699}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-21869-9_93}, DOI={10.1007/978-3-642-21869-9_93}, abstractNote={Off-task behavior is the subject of increasing interest in the AI in Education community. This paper reports on an investigation of the role of off-task behavior in narrative-centered learning environments by examining its interactions with student learning gains and affect. Results from an empirical study of students interacting with the Crystal Island environment indicate that off-task behavior generally has negative impacts on learning. However, further analyses of students’ affective transitions suggest that some students may be using off-task behavior as a strategy to regulate negative emotions.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Sabourin, Jennifer and Rowe, Jonathan P. and Mott, Bradford W. and Lester, James C.}, year={2011}, pages={534–536} }
 @inbook{boyer_phillips_ingram_ha_wallis_vouk_lester_2010, title={Characterizing the Effectiveness of Tutorial Dialogue with Hidden Markov Models}, ISBN={9783642133879 9783642133886}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-13388-6_10}, DOI={10.1007/978-3-642-13388-6_10}, abstractNote={Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This paper addresses that challenge through a machine learning approach that 1) learns tutorial strategies from a corpus of human tutoring, and 2) identifies the statistical relationships between student outcomes and the learned strategies. We have applied hidden Markov modeling to a corpus of annotated task-oriented tutorial dialogue to learn one model for each of two effective human tutors. We have identified significant correlations between the automatically extracted tutoring modes and student learning outcomes. This work has direct applications in authoring data-driven tutorial dialogue system behavior and in investigating the effectiveness of human tutoring.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Boyer, Kristy Elizabeth and Phillips, Robert and Ingram, Amy and Ha, Eun Young and Wallis, Michael and Vouk, Mladen and Lester, James}, year={2010}, pages={55–64} }
 @inbook{robison_mcquiggan_lester_2010, title={Developing Empirically Based Student Personality Profiles for Affective Feedback Models}, ISBN={9783642133879 9783642133886}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-13388-6_33}, DOI={10.1007/978-3-642-13388-6_33}, abstractNote={The impact of affect on learning has been the subject of increasing attention. Because of the differential effects of students’ affective states on learning outcomes, there is a growing recognition of the important role that intelligent tutoring systems can play in providing affective feedback to students. Although we are only beginning to understand the complex interactions between affect, feedback, and learning, it is evident that affective interventions can both positively and negatively influence learning experiences. To investigate how student personality traits can be used to predict responses to affective feedback, this paper presents an analysis of a large student affect corpus collected from three separate studies. Student personality profiles augmented with goal orientation and empathetic tendency information were analyzed with respect to affect state transitions. The results indicate that student personality profiles can serve as a powerful tool for informing affective feedback models.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Robison, Jennifer and McQuiggan, Scott and Lester, James}, year={2010}, pages={285–295} }
 @inbook{rowe_shores_mott_lester_2010, title={Integrating Learning and Engagement in Narrative-Centered Learning Environments}, ISBN={9783642134364 9783642134371}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-13437-1_17}, DOI={10.1007/978-3-642-13437-1_17}, abstractNote={A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments and traditional learning systems. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to learn, while on the other hand, traditional learning technologies may promote deep learning but provide limited engagement. This paper presents findings from a study with human participants that challenges the view that engagement and learning need be opposed. A study was conducted with 153 middle school students interacting with a narrative-centered learning environment. Rather than finding an oppositional relationship between learning and engagement, the study found a strong positive relationship between learning outcomes and increased engagement. Furthermore, the relationship between learning outcomes and engagement held even when controlling for students’ background knowledge and game-playing experience.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Rowe, Jonathan P. and Shores, Lucy R. and Mott, Bradford W. and Lester, James C.}, year={2010}, pages={166–177} }
 @inbook{lee_mott_lester_2010, title={Optimizing Story-Based Learning: An Investigation of Student Narrative Profiles}, ISBN={9783642134364 9783642134371}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-13437-1_16}, DOI={10.1007/978-3-642-13437-1_16}, abstractNote={Narrative-centered learning environments offer significant potential for creating effective learning experiences in which students actively participate in engaging story-based problem solving. As the capabilities of narrative-centered learning environments expand, a key challenge is identifying experiential factors that contribute to the most effective story-based learning. To investigate the impact of students’ narrative experiences on learning outcomes, a Wizard of Oz (WOZ) study was conducted with middle school students interacting with a narrative-centered learning environment. Students’ experiences were examined using narrative profiles representing their type of story interaction. With narrative planning, tutorial planning, and natural language dialogue functionalities provided by wizards, the WOZ study revealed that in interactive story-based learning supported by beyond-state-of-the-art ITS capabilities, 1) students exhibit a range of learning outcomes, 2) students exhibit a range of narrative profiles, and 3) certain student narrative profiles are strongly associated with desirable learning outcomes. The study suggests design decisions for optimizing story-based learning.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Lee, Seung Y. and Mott, Bradford W. and Lester, James C.}, year={2010}, pages={155–165} }
 @article{boyer_phillips_wallis_vouk_lester_2009, title={Investigating the role of student motivation in computer science education through one-on-one tutoring}, volume={19}, ISSN={0899-3408 1744-5175}, url={http://dx.doi.org/10.1080/08993400902937584}, DOI={10.1080/08993400902937584}, abstractNote={The majority of computer science education research to date has focused on purely cognitive student outcomes. Understanding the motivational states experienced by students may enhance our understanding of the computer science learning process, and may reveal important instructional interventions that could benefit student engagement and retention. This article investigates issues of student motivation as they arise during one-on-one human tutoring in introductory computer science. The findings suggest that the choices made during instructional discourse are associated with cognitive and motivational outcomes, and that particular strategies can be leveraged based on an understanding of the student motivational state.}, number={2}, journal={Computer Science Education}, publisher={Informa UK Limited}, author={Boyer, Kristy Elizabeth and Phillips, Robert and Wallis, Michael D. and Vouk, Mladen A. and Lester, James C.}, year={2009}, month={Jun}, pages={111–135} }
 @inbook{robison_rowe_mcquiggan_lester_2009, title={Predicting User Psychological Characteristics from Interactions with Empathetic Virtual Agents}, ISBN={9783642043796 9783642043802}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-04380-2_36}, DOI={10.1007/978-3-642-04380-2_36}, abstractNote={Enabling virtual agents to quickly and accurately infer users’ psychological characteristics such as their personality could support a broad range of applications in education, training, and entertainment. With a focus on narrative-centered learning environments, this paper presents an inductive framework for inferring users’ psychological characteristics from observations of their interactions with virtual agents. Trained on traces of users’ interactions with virtual agents in the environment, psychological user models are induced from the interactions to accurately infer different aspects of a user’s personality. Further, analyses of timing data suggest that these induced models are also able to converge on correct predictions after a relatively small number of interactions with virtual agents.}, booktitle={Intelligent Virtual Agents}, publisher={Springer Berlin Heidelberg}, author={Robison, Jennifer and Rowe, Jonathan and McQuiggan, Scott and Lester, James}, year={2009}, pages={330–336} }
 @inbook{mcquiggan_robison_lester_2008, title={Affective Transitions in Narrative-Centered Learning Environments}, ISBN={9783540691303 9783540691327}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-69132-7_52}, DOI={10.1007/978-3-540-69132-7_52}, abstractNote={Affect has been the subject of increasing attention in cognitive accounts of learning. Many intelligent tutoring systems now seek to adapt pedagogy to student affective and motivational processes in an effort to increase the effectiveness of tutorial interaction and improve learning outcomes. To this end, recent work has begun to investigate the emotions experienced during learning in a variety of environments. In this paper we extend this line of research by investigating the affective transitions that occur throughout narrative-centered learning experiences. Further analysis differentiates the likelihood of affective transitions stemming from pedagogical agent empathetic responses to student affect.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={McQuiggan, Scott W. and Robison, Jennifer L. and Lester, James C.}, year={2008}, month={Aug}, pages={490–499} }
 @inbook{rowe_ha_lester_2008, title={Archetype-Driven Character Dialogue Generation for Interactive Narrative}, ISBN={9783540854821 9783540854838}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-85483-8_5}, DOI={10.1007/978-3-540-85483-8_5}, abstractNote={Recent years have seen a growing interest in creating virtual agents to populate the cast of characters for interactive narrative. A key challenge posed by interactive characters for narrative environments is devising expressive dialogue generators. To be effective, character dialogue generators must be able to simultaneously take into account multiple sources of information that bear on dialogue, including character attributes, plot development, and communicative goals. Building on the narrative theory of character archetypes, we propose an archetype-driven character dialogue generator that uses a probabilistic unification framework to generate dialogue motivated by character personality and narrative history to achieve communicative goals. The generator’s behavior is illustrated with character dialogue generation in a narrative-centered learning environment, Crystal Island.}, booktitle={Intelligent Virtual Agents}, publisher={Springer Berlin Heidelberg}, author={Rowe, Jonathan P. and Ha, Eun Young and Lester, James C.}, year={2008}, month={Aug}, pages={45–58} }
 @inbook{boyer_phillips_wallis_vouk_lester_2008, title={Balancing Cognitive and Motivational Scaffolding in Tutorial Dialogue}, ISBN={9783540691303 9783540691327}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-69132-7_28}, DOI={10.1007/978-3-540-69132-7_28}, abstractNote={A key challenge in the design of tutorial dialogue systems is identifying tutorial strategies that can effectively balance the tradeoffs between cognitive and affective student outcomes. This balance is problematic because the precise nature of the interdependence between cognitive and affective strategies is not well understood. Furthermore, previous studies suggest that some cognitive and motivational goals are at odds with one another because a tutorial strategy designed to maximize one may negatively impact the other. This paper reports on a tutorial dialogue study that investigates motivational strategies and cognitive feedback. It was found that the choice of corrective tutorial strategy makes a significant difference in the outcomes of both student learning gains and self-efficacy gains.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Boyer, Kristy Elizabeth and Phillips, Robert and Wallis, Michael and Vouk, Mladen and Lester, James}, year={2008}, month={Aug}, pages={239–249} }
 @article{mcquiggan_mott_lester_2008, title={Modeling self-efficacy in intelligent tutoring systems: An inductive approach}, volume={18}, ISSN={["1573-1391"]}, DOI={10.1007/s11257-007-9040-y}, number={1-2}, journal={USER MODELING AND USER-ADAPTED INTERACTION}, author={McQuiggan, Scott W. and Mott, Bradford W. and Lester, James C.}, year={2008}, month={Feb}, pages={81–123} }
 @inbook{mcquiggan_rowe_lee_lester_2008, title={Story-Based Learning: The Impact of Narrative on Learning Experiences and Outcomes}, ISBN={9783540691303 9783540691327}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-69132-7_56}, DOI={10.1007/978-3-540-69132-7_56}, abstractNote={Within the intelligent tutoring systems community, narrative is emerging as an effective medium for contextualizing learning. To date, relatively few empirical studies have been conducted to assess learning in narrative-centered learning environments. In this paper, we investigate the effect of narrative on learning experiences and outcomes. We present results from an experiment conducted with eighth-grade middle school students interacting with a narrative-centered learning environment in the domain of microbiology. The study found that students do exhibit learning gains, that those gains are less than those produced by traditional instructional approaches, but that the motivational benefits of narrative-centered learning with regard to self-efficacy, presence, interest, and perception of control are substantial.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={McQuiggan, Scott W. and Rowe, Jonathan P. and Lee, Sunyoung and Lester, James C.}, year={2008}, month={Aug}, pages={530–539} }
 @inbook{mcquiggan_goth_ha_rowe_lester_2008, title={Student Note-Taking in Narrative-Centered Learning Environments: Individual Differences and Learning Effects}, ISBN={9783540691303 9783540691327}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-69132-7_54}, DOI={10.1007/978-3-540-69132-7_54}, abstractNote={Note-taking has a long history in educational settings. Previous research has shown that note-taking leads to improved learning and performance on assessment. It was therefore hypothesized that note-taking could play an important role in narrative-centered learning. To investigate this question, a note-taking facility was introduced into a narrative-centered learning environment. Students were able to use the facility to take and review notes while solving a science mystery. In this paper we explore the individual differences of note-takers and the notes they take. Finally, we use machine learning techniques to model the content of student notes to support future pedagogical adaptation in narrative-centered learning environments.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={McQuiggan, Scott W. and Goth, Julius and Ha, Eunyoung and Rowe, Jonathan P. and Lester, James C.}, year={2008}, month={Aug}, pages={510–519} }
 @inbook{mcquiggan_lee_lester_2007, title={Early Prediction of Student Frustration}, ISBN={9783540748885 9783540748892}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-74889-2_61}, DOI={10.1007/978-3-540-74889-2_61}, abstractNote={Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately.}, booktitle={Affective Computing and Intelligent Interaction}, publisher={Springer Berlin Heidelberg}, author={McQuiggan, Scott W. and Lee, Sunyoung and Lester, James C.}, year={2007}, month={Sep}, pages={698–709} }
 @inbook{lee_mcquiggan_lester_2007, title={Inducing User Affect Recognition Models for Task-Oriented Environments}, ISBN={9783540730774 9783540730781}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-73078-1_49}, DOI={10.1007/978-3-540-73078-1_49}, abstractNote={Accurately recognizing users’ affective states could contribute to more productive and enjoyable interactions, particularly for task-oriented learning environments. In addition to using physiological data, affect recognition models can leverage knowledge of task structure and user goals to effectively reason about users’ affective states. In this paper we present an inductive approach to recognizing users’ affective states based on appraisal theory, a motivational-affect account of cognition in which individuals’ emotions are generated in response to their assessment of how their actions and events in the environment relate to their goals. Rather than manually creating the models, the models are learned from training sessions in which (1) physiological data, (2) information about users’ goals and actions, and (3) environmental information are recorded from traces produced by users performing a range of tasks in a virtual environment. An empirical evaluation with a task-oriented learning environment testbed suggests that an inductive approach can learn accurate models and that appraisal-based models exploiting knowledge of task structure and user goals can outperform purely physiologically-based models.}, booktitle={User Modeling 2007}, publisher={Springer Berlin Heidelberg}, author={Lee, Sunyoung and McQuiggan, Scott W. and Lester, James C.}, year={2007}, month={Aug}, pages={380–384} }
 @article{mcquiggan_lester_2007, title={Modeling and evaluating empathy in embodied companion agents}, volume={65}, ISSN={["1095-9300"]}, DOI={10.1016/j.ijhcs.2006.11.015}, abstractNote={Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify their own affective state accordingly, and then respond to these outcomes by expressing empathetic behaviors. Synthetic agents serving as companions should respond similarly. However, empathetic reasoning is riddled with the complexities stemming from the myriad factors bearing upon situational assessment. A key challenge posed by affective reasoning in synthetic agents is devising empirically informed models of empathy that accurately respond in social situations. This paper presents Care, a data-driven affective architecture and methodology for learning models of empathy by observing human–human social interactions. First, in Care training sessions, one trainer directs synthetic agents to perform a sequence of tasks while another trainer manipulates companion agents’ affective states to produce empathetic behaviors (spoken language, gesture, and posture). Care tracks situational data including locational, intentional, and temporal information to induce a model of empathy. At runtime, Care uses the model of empathy to drive situation-appropriate empathetic behaviors. Care has been used in a virtual environment testbed. Two complementary studies investigating the predictive accuracy and perceived accuracy of Care-induced models of empathy suggest that the Care paradigm can provide the basis for effective empathetic behavior control in embodied companion agents.}, number={4}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES}, author={McQuiggan, Scott W. and Lester, James C.}, year={2007}, month={Apr}, pages={348–360} }
 @article{mcquiggan_lester_2006, title={Diagnosing self-efficacy in intelligent tutoring systems: An empirical study}, DOI={10.1007/11774303_56}, abstractNote={Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a students’ level of self-efficacy. This paper investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. In an empirical study, two families of self-efficacy models were induced: a static model, learned solely from pre-test (non-intrusively collected) data, and a dynamic model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. The resulting static model is able to predict students’ real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate.}, number={4053}, journal={Lecture Notes in Computer Science}, author={McQuiggan, S. W. and Lester, J. C.}, year={2006}, pages={565–574} }
 @article{mott_lester_2006, title={Narrative-centered tutorial planning for inquiry-based learning environments}, DOI={10.1007/11774303_67}, abstractNote={Recent years have seen growing interest in narrative-centered learning environments. Leveraging the inherent structure of narrative, narrative-centered learning environments offer significant potential for inquiry-based learning in which students actively participate in engaging story-based problem-solving. A key challenge posed by narrative-centered learning is orchestrating all of the events in the unfolding story to motivate students and promote effective learning. In this paper we present a narrative-centered tutorial planning architecture that integrates narrative planning and pedagogical control. The architecture continually constructs and updates narrative plans to support the hypothesis-generation-testing cycles that form the basis for inquiry-based learning. It is being used to implement a prototype narrative-centered inquiry-based learning environment for the domain of microbiology. The planner dynamically balances narrative and pedagogical goals while at the same time satisfying the real-time constraints of highly interactive learning environments.}, number={4053}, journal={Lecture Notes in Computer Science}, author={Mott, B. W. and Lester, J. C.}, year={2006}, pages={675–684} }
 @inbook{branting_lester_mott_2004, title={Dialogue Management for Conversational Case-Based Reasoning}, ISBN={9783540228820 9783540286318}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-28631-8_7}, DOI={10.1007/978-3-540-28631-8_7}, abstractNote={Two key objectives of conversational case-based reasoning (CCBR) systems are (1) eliciting case facts in a manner that minimizes the user’s burden in terms of resources such as time, information cost, and cognitive load, and (2) integrating CBR with other problem solving modalities. This paper proposes an architecture that addresses both these goals by integrating CBR with a discourse-oriented dialogue engine. The dialogue engine determines when CBR or other problem-solving techniques are needed to achieve pending discourse goals. Conversely, the CBR component has the full resources of a dialogue engine to handle topic changes, interruptions, clarification questions by either the user or the system, and other speech acts that arise in problem-solving dialogues.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Berlin Heidelberg}, author={Branting, Karl and Lester, James and Mott, Bradford}, year={2004}, pages={77–90} }
 @inbook{frasson_porayska-pomsta_conati_gouarderes_johnson_pain_andre_bickmore_brna_de castro_et al._2004, title={Workshop on Social and Emotional Intelligence in Learning Environments}, ISBN={9783540229483 9783540301394}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-30139-4_125}, DOI={10.1007/978-3-540-30139-4_125}, abstractNote={It has been long recognised in education that teaching and learning is a highly social and emotional activity. Students’ cognitive progress depends on their psychological predispositions such as their interest, confidence, sense of progress and achievement as well as on social interactions with their teachers and peers who provide them (or not) with both cognitive and emotional support. Until recently the ability to recognise students’ socio-affective needs constituted exclusively the realm of human tutors’ social competence. However, in recent years and with the development of more sophisticated computer-aided learning environments, the need for those environments to take into account the student’s affective states and traits and to place them within the context of the social activity of learning has become an important issue in the domain of building intelligent and effective learning environments. More recently, the notion of emotional intelligence has attracted increasing attention as one of tutors’ pre-requisites for improving students’ learning.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Frasson, Claude and Porayska-Pomsta, Kaska and Conati, Cristina and Gouarderes, Guy and Johnson, Lewis and Pain, Helen and Andre, Elisabeth and Bickmore, Tim and Brna, Paul and de Castro, Isabel Fernandez and et al.}, year={2004}, pages={913–913} }
 @article{callaway_lester_2002, title={Narrative prose generation}, volume={139}, ISSN={["1872-7921"]}, DOI={10.1016/s0004-3702(02)00230-8}, abstractNote={Narrative generation has historically suffered from poor writing quality, stemming from a narrow focus on story grammars and plot design. Moreover, to-date natural language generation systems have not been capable of faithfully reproducing either the variety or complexity of naturally occurring narratives. In this article we first propose a model of narrative derived from work in narratology and grounded in observed linguistic phenomena. Next we describe the Author architecture for narrative generation and an end-to-end implementation of the Author model in the StoryBook narrative prose generation system. Finally, we present a formal evaluation of the narratives that StoryBook produces.}, number={2}, journal={ARTIFICIAL INTELLIGENCE}, author={Callaway, CB and Lester, JC}, year={2002}, month={Aug}, pages={213–252} }
 @article{moreno_mayer_spires_lester_2001, title={The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents?}, volume={19}, ISSN={["1532-690X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0000606137&partnerID=MN8TOARS}, DOI={10.1207/S1532690XCI1902_02}, abstractNote={College students (in Experiment 1) and 7th-grade students (in Experiment 2) learned how to design the roots, stem, and leaves of plants to survive in 8 different environments through a computer-based multimedia lesson. They learned by interacting with an animated pedagogical agent who spoke to them (Group PA) or received identical graphics and explanations as on-screen text without a pedagogical agent (Group No PA). Group PA outperformed Group No PA on transfer tests and interest ratings but not on retention tests. To investigate further the basis for this personal agent effect, we varied the interactivity of the agent-based lesson (Experiment 3) and found an interactivity effect: Students who participate in the design of plant parts remember more and transfer what they have learned to solve new problems better than students who learn the same materials without participation. Next, we varied whether the agent's words were presented as speech or on-screen text, and whether the agent's image appeared on the screen. Both with a fictional agent (Experiment 4) and a video of a human face (Experiment 5), students performed better on tests of retention and problem-solving transfer when words were presented as speech rather than on-screen text (producing a modality effect) but visual presence of the agent did not affect test performance (producing no image effect). Results support the introduction of interactive pedagogical agents who communicate with students via speech to promote meaningful learning in multimedia lessons.}, number={2}, journal={COGNITION AND INSTRUCTION}, author={Moreno, R and Mayer, RE and Spires, HA and Lester, JC}, year={2001}, pages={177–213} }
 @article{lester_voerman_towns_callaway_1999, title={Deictic believability: Coordinated gesture, locomotion, and speech in lifelike pedagogical agents}, volume={13}, ISSN={["0883-9514"]}, DOI={10.1080/088395199117324}, abstractNote={Lifelike animated agents for knowledge - based learning environments can provide timely , customized advice to support students' problem solving . Because of their strong visual presence , they hold significant promise for substantially increasing students' enjoyment of their learning experiences . A key problemposed by lifelike agents that inhabit artificial worlds is deictic believability. In the same manner that humans refer to objects in their environment through judicious combinations of speech , locomotion , and gesture , animated agents should be able to move through their environment and point to and refer to objects appropriately as they provide problem - solving advice . In this paper we describe a framework for achieving deictic believability in animated agents . A deictic behavior planner exploits a world model and the evolving explanation plan as it selects and coordinates locomotive , gestural , and speech behaviors . The resulting behaviors and utterances are believable , and the references...}, number={4-5}, journal={APPLIED ARTIFICIAL INTELLIGENCE}, author={Lester, JC and Voerman, JL and Towns, SG and Callaway, CB}, year={1999}, pages={383–414} }
 @article{bares_lester_1999, title={Intelligent multi-shot 3D visualization interfaces}, volume={12}, ISSN={["1872-7409"]}, DOI={10.1016/S0950-7051(99)00034-9}, abstractNote={In next-generation virtual 3D simulation, training, and entertainment environments, intelligent visualization interfaces must respond to user-specified viewing requests so users can follow salient points of the action and monitor the relative locations of objects. Users should be able to indicate which object(s) to view, how each should be viewed, what cinematic style and pace to employ, and how to respond when a single satisfactory view is not possible. When constraints fail, weak constraints can be relaxed or multi-shot solutions can be displayed in sequence or as composite shots with simultaneous viewports. To address these issues, we have developed ConstraintCam, a real-time camera visualization interface for dynamic 3D worlds.}, number={8}, journal={KNOWLEDGE-BASED SYSTEMS}, author={Bares, WH and Lester, JC}, year={1999}, month={Dec}, pages={403–412} }
 @article{lester_stone_stelling_1999, title={Lifelike pedagogical agents for mixed-initiative problem solving in constructivist learning environments}, volume={9}, ISSN={["1573-1391"]}, DOI={10.1023/A:1008374607830}, number={1-2}, journal={USER MODELING AND USER-ADAPTED INTERACTION}, author={Lester, JC and Stone, BA and Stelling, GD}, year={1999}, pages={1–44} }
 @inbook{bares_zettlemoyer_lester_1998, title={Habitable 3D learning environments for situated learning}, DOI={10.1007/3-540-68716-5_13}, abstractNote={The growing emphasis on learner-centered education focuses on intrinsically motivated learning via engaging problem-solving activities. Habitable 3D learning environments, in which learners guide avatars through virtual worlds for role-based problem solving, hold great promise for situated learning. We have been investigating habitable learning environments by iteratively designing, implementing, and evaluating them. In the Situated Avatar-Based Immersive Learning (SAIL) framework for habitable 3D learning environments, learners navigate avatars through virtual worlds as they solve problems by manipulating artifacts. The SAIL framework has been used to implement CPU CITY, a 3D learning environment testbed for the domain of computer architecture. A visually compelling virtual cityscape of computer components, CPU CITY presents learners with goal advertisements that focus their attention on salient problem-solving sub-tasks. The CPU CITY testbed has produced prototypes that have been evaluated. Pilot studies suggest that habitable learning environments offer a promising new paradigm for educational applications.}, booktitle={Intelligent tutoring systems: 4th International Conference, ITS '98, San Antonio, Texas, USA, August 16-19, 1998: Proceedings}, publisher={Berlin: Springer}, author={Bares, W. H. and Zettlemoyer, L. S. and Lester, J. C.}, year={1998}, pages={76–85} }
 @inbook{towns_fitzgerald_lester_1998, title={Visual emotive communication in lifelike pedagogical agents}, DOI={10.1007/3-540-68716-5_53}, abstractNote={Lifelike animated agents for knowledge-based learning environments can provide timely, customized advice to support leaners’ problem-solving activities. By drawing on a rich repertoire of emotive behaviors to exhibit contextually appropriate facial expressions and emotive gestures, these agents could exploit the visual channel to more effectively communicate with learners. To address these issues, this paper proposes the emotive-kinesthetic behavior sequencing framework for dynamically sequencing lifelike pedagogical agents’ full-body emotive expression. By exploiting a rich behavior space populated with emotive behaviors and structured by pedagogical speech act categories, a behavior sequencing engine operates in realtime to select and assemble contextually appropriate expressive behaviors. This framework has been implemented in a lifelike pedagogical agent, Cosmo, who exhibits full-body emotive behaviors in response to learners’ problem-solving activities.}, booktitle={Intelligent tutoring systems: 4th International Conference, ITS '98, San Antonio, Texas, USA, August 16-19, 1998: Proceedings}, publisher={Berlin: Springer}, author={Towns, S. G. and Fitzgerald, P. J. and Lester, J. C.}, year={1998}, pages={474–483} }
 @inproceedings{lester_fitzgerald_stone_1997, title={The pedagogical design studio: Exploiting artifact-based task models for constructivist learning}, DOI={10.1145/238218.238317}, abstractNote={Intelligent learning environments that support constructivism should provide active learning experiences that are customized for individual learners. To do so, they must determine learner intent and detect misconceptions, and this diagnosis must be performed as non-invasively as possible. To this end, we propose the pedagogical design studio, a design-centered framework for learning environment interfaces. Pedagogical design studios provide learners with a rich, direct manipulation design experience. By exploiting an artifact-based task model that preserves a tight mapping between the interface state and design sub-tasks, they non-invasively infer learners’ intent and detect misconceptions. The task model is then used to tailor problem presentation, produce a customized musical score, and modulate problem-solving intervention. To explore these notions, we have implemented a pedagogical design studio for a constructivist learning environment that provides instruction to middle school students about botanical anatomy and physiology. Evaluations suggest that the design studio framework constitutes an effective approach to interfaces that support constructivist learning.}, booktitle={IUI97: 1997 International Conference on Intelligent User Interfaces, January 6-9, 1997, Orlando, Florida, USA}, publisher={New York: Association for Computing Machinery}, author={Lester, J. C. and Fitzgerald, P. J. and Stone, B. A.}, editor={J. Moore, E. Ernest and Puerta, A.Editors}, year={1997}, pages={155–162} }
 @inproceedings{ocumpaugh_andres_baker_defalco_paquette_rowe_mott_lester_georgoulas_brawner_et al., title={Affect dynamics in military trainees using vMedic: From engaged concentration to boredom to confusion}, volume={10331}, booktitle={Artificial intelligence in education, aied 2017}, author={Ocumpaugh, J. and Andres, J. M. and Baker, R. and DeFalco, J. and Paquette, L. and Rowe, J. and Mott, B. and Lester, J. and Georgoulas, V. and Brawner, K. and et al.}, pages={238–249} }
 @article{mcquiggan_robison_lester, title={Affective transitions in narrative-centered learning environments}, volume={13}, number={1}, journal={Educational Technology & Society}, author={McQuiggan, S. W. and Robison, J. L. and Lester, J. C.}, pages={40–53} }
 @inproceedings{rowe_ha_lester, title={Archetype-driven character dialogue generation for interactive narrative}, volume={5208}, booktitle={Intelligent virtual agents, proceedings}, author={Rowe, J. P. and Ha, E. Y. and Lester, J. C.}, pages={45–58} }
 @inproceedings{sawyer_rowe_lester, title={Balancing learning and engagement in game-based learning environments with multi-objective reinforcement learning}, volume={10331}, booktitle={Artificial intelligence in education, aied 2017}, author={Sawyer, R. and Rowe, J. and Lester, J.}, pages={323–334} }
 @inproceedings{smith_min_mott_lester, title={Diagrammatic student models: Modeling student drawing performance with deep learning}, volume={9146}, booktitle={User modeling, adaptation and personalization}, author={Smith, A. and Min, W. and Mott, B. W. and Lester, J. C.}, pages={216–227} }
 @inproceedings{boyer_ha_wallis_phillips_vouk_lester, title={Discovering tutorial dialogue strategies with hidden Markov models}, volume={200}, booktitle={Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling }, author={Boyer, K. E. and Ha, E. Y. and Wallis, M. D. and Phillips, R. and Vouk, M. A. and Lester, J. C.}, pages={141–148} }
 @inproceedings{rowe_lester, title={Improving student problem solving in narrative-centered learning environments: A modular reinforcement learning framework}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Rowe, J. P. and Lester, J. C.}, pages={419–428} }
 @article{lester, title={Introduction to the special issue on intelligent user interfaces}, volume={22}, number={4}, journal={AI Magazine}, author={Lester, J.}, pages={13} }
 @inproceedings{sawyer_smith_rowe_azevedo_lester, title={Is more agency better? The impact of student agency on game-based learning}, volume={10331}, booktitle={Artificial intelligence in education, aied 2017}, author={Sawyer, R. and Smith, A. and Rowe, J. and Azevedo, R. and Lester, J.}, pages={335–346} }
 @inproceedings{grafsgaard_lee_mott_boyer_lester, title={Modeling self-efficacy across age groups with automatically tracked facial expression}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Grafsgaard, J. F. and Lee, S. Y. and Mott, B. W. and Boyer, K. E. and Lester, J. C.}, pages={582–585} }
 @inproceedings{robison_mcquiggan_lester, title={Modeling task-based vs. affect-based feedback behavior in pedagogical agents: An inductive approach}, volume={200}, booktitle={Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling }, author={Robison, J. L. and McQuiggan, S. W. and Lester, J. C.}, pages={25–32} }
 @inproceedings{rowe_mcquiggan_robison_lester, title={Off-task behavior in narrative-centered learning environments}, volume={200}, booktitle={Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling }, author={Rowe, J. P. and McQuiggan, S. W. and Robison, J. L. and Lester, J. C.}, pages={99–106} }
 @article{johnson_lester, title={Pedagogical agents: Back to the future}, volume={39}, number={2}, journal={AI Magazine}, author={Johnson, W. L. and Lester, J. C.}, pages={33–44} }
 @inproceedings{mudrick_taub_azevedo_rowe_lester, title={Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion}, booktitle={International conference on affective computing and intelligent}, author={Mudrick, N. V. and Taub, M. and Azevedo, R. and Rowe, J. and Lester, J.}, pages={184–189} }
 @inproceedings{taub_mudrick_azevedo_millar_rowe_lester, title={Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Mudrick, N. V. and Azevedo, R. and Millar, G. C. and Rowe, J. and Lester, J.}, pages={240–246} }
 @inproceedings{taub_mudrick_azevedo_millar_rowe_lester, title={Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with crystal island}, volume={9684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Mudrick, N. V. and Azevedo, R. and Millar, G. C. and Rowe, J. and Lester, J.}, pages={240–246} }
 @inbook{frasson_porayska-pomsta_conati_gouarderes_johnson_pain_andre_bickmore_brna_de castro_et al., title={Workshop on social and emotional intelligence in learning environments}, volume={3220}, ISBN={3540229485}, booktitle={Intelligent tutoring systems: 7th International Conference, ITS 2004, Maceio, Alagoas, Brazil, August 30-September 3, 2004: Proceedings}, publisher={Berlin; New York: Springer}, author={Frasson, C. and Porayska-Pomsta, K. and Conati, C. and Gouarderes, G. and Johnson, L. and Pain, H. and Andre, E. and Bickmore, T. and Brna, P. and De Castro, I. F. and et al.}, editor={J. C Lester, R. M. Vicari and Paraguacu, F.Editors}, pages={913} }