@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{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{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{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_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{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{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{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{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{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{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{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{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{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} }
 @article{barot_buro_cook_eladhari_johansson_li_liapis_mccoy_ontanon_rowe_et al._2016, title={The AIIDE 2015 Workshop Program}, volume={37}, ISSN={["0738-4602"]}, DOI={10.1609/aimag.v37i2.2660}, abstractNote={The workshop program at the 11th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015, at the University of California, Santa Cruz, USA. The program included four workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real‐Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.}, number={2}, journal={AI MAGAZINE}, author={Barot, Camille and Buro, Michael and Cook, Michael and Eladhari, Mirjam and Johansson, Magnus and Li, Boyang and Liapis, Antonios and McCoy, Josh and Ontanon, Santiago and Rowe, Jonathan and et al.}, year={2016}, pages={91–94} }
 @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} }
 @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} }
 @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} }
 @article{elson_rowe_smith_smith_tomai_2012, title={Reports on the 2011 AAAI Fourth Artificial Intelligence for Interactive Digital Entertainment Conference Workshops}, volume={33}, ISSN={["0738-4602"]}, DOI={10.1609/aimag.v33i1.2393}, abstractNote={The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE‐11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one‐day workshops were held on October 11: Intelligent Narrative Technologies, and Artificial Intelligence in the Game Design Process. The highlights of each workshop are presented in this report.}, number={1}, journal={AI MAGAZINE}, author={Elson, David and Rowe, Jonathan and Smith, Adam M. and Smith, Gillian and Tomai, Emmett}, year={2012}, pages={55–56} }
 @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} }
 @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{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} }
 @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{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} }
 @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} }