@inproceedings{goslen_gupta_muthukrishnan_midgett_min_vandenberg_cateté_mott_2024, title={Engaging Students from Rural Communities in AI Education with Game-Based Learning}, url={https://doi.org/10.1145/3626253.3635549}, DOI={10.1145/3626253.3635549}, abstractNote={As the presence of artificial intelligence (AI) technologies increases throughout everyday life, so does the need to engage rural communities in AI learning experiences, as these communities often have limited access to such educational opportunities. This work presents three game-based learning activities rooted in core AI concepts: natural language processing, search, and reinforcement learning. These activities were implemented in a summer camp with middle grades students in a rural area of the USA. We share an overview of the activities, as well as key observations and takeaways from student responses in post-activity surveys.}, author={Goslen, Alex and Gupta, Anisha and Muthukrishnan, Smrithi and Midgett, Raven and Min, Wookhee and Vandenberg, Jessica and Cateté, Veronica and Mott, Bradford}, year={2024}, month={Mar} }
 @inproceedings{lim_min_vandenberg_cateté_uchidiuno_mott_2024, title={Supporting Student Engagement in K-12 AI Education with a Card Game Construction Toolkit}, url={https://doi.org/10.1145/3626253.3635550}, DOI={10.1145/3626253.3635550}, abstractNote={With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging AI learning activities. In this paper, we present our work on a game construction toolkit for middle school students and educators that enables them to tailor an AI-focused unplugged card game activity. In our prior work, we designed, developed, and piloted an unplugged card game activity where players predict the identity of a person based on hand-drawn features extracted from a set of facial cards. The activity aims to teach AI concepts aligned with one of the big ideas in AI utilizing techniques from facial recognition. During our pilot testing of the activity, we discovered that creating face cards that capture students' interest is a crucial factor in promoting student engagement. As a result, we designed a card game construction toolkit that allows students and educators to craft their own face card decks using photos that are personally interesting to them, looking to foster engagement and improve replayability of the activity. The toolkit's design is focused on ensuring easy accessibility and features a simple web-based interface that allows users to download and print their customized cards. We expect this toolkit will enhance the usability and educational effectiveness of our unplugged K-12 AI education activity.}, author={Lim, Hansol and Min, Wookhee and Vandenberg, Jessica and Cateté, Veronica and Uchidiuno, Judith and Mott, Bradford}, year={2024}, month={Mar} }
 @article{vandenberg_min_catete_boulden_mott_2023, title={Leveraging Game Design Activities for Middle Grades AI Education in Rural Communities}, url={https://doi.org/10.1145/3582437.3587193}, DOI={10.1145/3582437.3587193}, abstractNote={The ever pervasive nature of artificial intelligence (AI) in our world necessitates a focus on fostering an AI literate society. Young children, those aged 11 to 14, are at a critical point in developing their dispositions toward and perceptions of science, technology, engineering, and mathematics (STEM), which influences their future education and career interests. Youth in rural areas are in particular need of access to AI learning opportunities to prepare them for the future workforce; digital games may be one way to attract young, rural students to STEM education and careers. In this paper, we explore how to introduce rural middle grades students to foundational AI concepts through digital game design activities. To inform our efforts and to establish an understanding of what these student populations as well as their teachers know about AI and games, we conducted a set of interviews and focus groups. In brief, students’ awareness and understanding of AI varied significantly, whereas teachers had limited knowledge of AI. Moreover, students shared great interest in playing and designing games. In support of our findings, we are developing a set of game design activities around five core AI concepts and ensuring the activities are of interest to our rural students.}, journal={PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF DIGITAL GAMES, FDG 2023}, author={Vandenberg, Jessica and Min, Wookhee and Catete, Veronica and Boulden, Danielle and Mott, Bradford}, year={2023} }
 @inproceedings{emerson_min_rowe_azevedo_lester_2023, title={Multimodal Predictive Student Modeling with Multi-Task Transfer Learning}, url={https://doi.org/10.1145/3576050.3576101}, DOI={10.1145/3576050.3576101}, abstractNote={Game-based learning environments have the distinctive capacity to promote learning experiences that are both engaging and effective. Recent advances in sensor-based technologies (e.g., facial expression analysis and eye gaze tracking) and natural language processing have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics and student modeling informed by multimodal data captured during students’ interactions with game-based learning environments hold significant promise for designing effective learning environments that detect unproductive student behaviors and provide adaptive support for students during learning. Learning analytics frameworks that can accurately predict student learning outcomes early in students’ interactions hold considerable promise for enabling environments to dynamically adapt to individual student needs. In this paper, we investigate a multimodal, multi-task predictive student modeling framework for game-based learning environments. The framework is evaluated on two datasets of game-based learning interactions from two student populations (n=61 and n=118) who interacted with two versions of a game-based learning environment for microbiology education. The framework leverages available multimodal data channels from the datasets to simultaneously predict student post-test performance and interest. In addition to inducing models for each dataset individually, this work investigates the ability to use information learned from one source dataset to improve models based on another target dataset (i.e., transfer learning using pre-trained models). Results from a series of ablation experiments indicate the differences in predictive capacity among a combination of modalities including gameplay, eye gaze, facial expressions, and reflection text for predicting the two target variables. In addition, multi-task models were able to improve predictive performance compared to single-task baselines for one target variable, but not both. Lastly, transfer learning showed promise in improving predictive capacity in both datasets.}, author={Emerson, Andrew and Min, Wookhee and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2023}, month={Mar} }
 @article{vandenberg_min_gupta_catete_boulden_mott_2023, title={Toward AI-infused Game Design Activities for Rural Middle Grades Students}, url={https://doi.org/10.1145/3587103.3594199}, DOI={10.1145/3587103.3594199}, abstractNote={The ubiquity of artificial intelligence (AI) in everyday life suggests the need to ensure young students know about AI, its uses and limitations, and its benefits and risks, while enabling them to develop expertise in using AI-driven technologies. To support rural middle grades students and educators in learning and teaching AI concepts, we are designing AI-focused learning activities centered around the creation of digital gameplay experiences. To inform our designs, we conducted educator interviews and student focus groups to gain insights into their understanding of AI, their computer science background, and their knowledge and interest in gaming. Building on findings from these interviews and focus groups, we have designed a set of hands-on activities to elicit deeper feedback from students and educators on their preferences, points of confusion, and interests. In this work, we present our initial AI-infused game design activities.}, journal={PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL. 2}, author={Vandenberg, Jessica and Min, Wookhee and Gupta, Anisha and Catete, Veronica and Boulden, Danielle and Mott, Bradford}, year={2023}, pages={644–644} }
 @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={AbstractGame‐based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game‐based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine‐grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post‐gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine‐grained individual student performance in game‐based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game‐based learning environment for microbiology, Crystal Island, we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post‐test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question‐related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions.
Practitioner notesWhat is already known about this topic

A distinctive characteristic of game‐based learning environments is their capacity to enable fine‐grained student assessment.
Adaptive game‐based learning environments offer individualisation based on specific student needs and should be able to assess student competencies using early prediction models of those competencies.
Word embedding approaches from the field of natural language processing show great promise in the ability to encode semantic information that can be leveraged by predictive student models.
What this paper adds

Investigates word embeddings of assessment question content for reliable early prediction of student performance.
Demonstrates the efficacy of distributed word embeddings of assessment questions when used by early prediction models compared to models that use either no assessment information or discrete representations of the questions.
Demonstrates the efficacy and generalisability of word embeddings of assessment questions for predicting the performance of both new students on existing questions and existing students on new questions.
Implications for practice and/or policy

Word embeddings of assessment questions can enhance early prediction models of student knowledge, which can drive adaptive feedback to students who interact with game‐based learning environments.
Practitioners should determine if new assessment questions will be developed for their game‐based learning environment, and if so, consider using our student modelling framework that incorporates early prediction models pretrained with existing student responses to previous assessment questions and is generalisable to the new assessment questions by leveraging distributed word embedding techniques.
Researchers should consider the most appropriate way to encode the assessment questions in ways that early prediction models are able to infer relationships between the questions and gameplay behaviour to make accurate predictions of student competencies.

}, 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{vandenberg_min_catete_boulden_mott_2023, title={Promoting AI Education for Rural Middle Grades Students with Digital Game Design}, url={https://doi.org/10.1145/3545947.3576333}, DOI={10.1145/3545947.3576333}, abstractNote={The demand is growing for a populace that is literate in Artificial Intelligence (AI); such literacy centers on enabling individuals to evaluate, collaborate with, and effectively use AI. Because the middle school years are a critical time for developing youths' perceptions and dispositions toward STEM, creating engaging AI learning experiences for middle grades students (ages 11 to 14) is paramount. The need for providing enhanced access to AI learning opportunities is especially pronounced in rural areas, which are typically underserved and underresourced. Inspired by prior research that game design holds significant potential for cultivating student interest and knowledge in computer science, we are designing, developing, and iteratively refining an AI-centered development environment that infuses AI learning into game design activities. In this work, we review design principles for game design interventions focused on middle grades computer science education and explore how to introduce AI learning experiences into interactive game design activities. We also discuss results from our initial co-design sessions with middle grades students and teachers in rural communities.}, journal={PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 2, SIGCSE 2023}, author={Vandenberg, Jessica and Min, Wookhee and Catete, Veronica and Boulden, Danielle and Mott, Bradford}, year={2023}, pages={1388–1388} }
 @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{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{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} }
 @inproceedings{akram_min_wiebe_navied_mott_boyer_lester_2020, title={A conceptual assessment framework for k-12 computer science rubric design}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081624104&partnerID=MN8TOARS}, DOI={10.1145/3328778.3372643}, abstractNote={The lack of effective guidelines for assessing students' computer science (CS) competencies is creating significant demand by K-12 teachers for CS assessments to evaluate students' learning. We propose a conceptual assessment framework that guides teachers through designing appropriate assessments for computer science (CS) activities in their classrooms. The framework addresses the critical problem of incorporating CS into K-12 curricula without corresponding assessments. We illustrate its use with the design of a rubric for a bubble sort algorithm situated in a game-based learning environment for middle-grade students. We also apply a preliminary and a revised version of this assessment on two datasets collected from students' interactions with the learning environment. We found consistency among results identified through applying the preliminary and the revised rubric. The results reveal distinctive patterns in students' approaches to CS problem solving and coherency with respect to different aspects of the rubric.*}, booktitle={Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE}, author={Akram, B. and Min, W. and Wiebe, E. and Navied, A. and Mott, B. and Boyer, K.E. and Lester, J.}, year={2020}, pages={1328} }
 @article{min_frankosky_mott_rowe_smith_wiebe_boyer_lester_2019, title={DeepStealth: Game-Based Learning Stealth Assessment with Deep Neural Networks}, volume={13}, ISSN={1939-1382 2372-0050}, url={http://dx.doi.org/10.1109/TLT.2019.2922356}, DOI={10.1109/TLT.2019.2922356}, abstractNote={A distinctive feature of game-based learning environments is their capacity for enabling stealth assessment. Stealth assessment analyzes a stream of fine-grained student interaction data from a game-based learning environment to dynamically draw inferences about students’ competencies through evidence-centered design. In evidence-centered design, evidence models have been traditionally designed using statistical rules authored by domain experts that are encoded using Bayesian networks. This article presents DeepStealth, a deep learning-based stealth assessment framework, that yields significant reductions in the feature engineering labor that has previously been required to create stealth assessments. DeepStealth utilizes end-to-end trainable deep neural network-based evidence models. Using this framework, evidence models are devised using a set of predictive features captured from raw, low-level interaction data to infer evidence for competencies. We investigate two deep learning-based evidence models, long short-term memory networks (LSTMs) and n-gram encoded feedforward neural networks (FFNNs). We compare these models’ predictive performance for inferring students’ knowledge to linear-chain conditional random fields (CRFs) and naïve Bayes models. We perform feature set-level analyses of game trace logs and external pre-learning measures, and we examine the models’ early prediction capacity. The framework is evaluated using data collected from 182 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors outperform competitive baseline approaches with respect to predictive accuracy and early prediction capacity. We find that LSTMs, FFNNs, and CRFs all benefit from combined feature sets derived from both game trace logs and external pre-learning measures.}, number={2}, journal={IEEE Transactions on Learning Technologies}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Min, Wookhee and Frankosky, Megan and Mott, Bradford W. and Rowe, Jonathan and Smith, Peter Andrew Miller and Wiebe, Eric and Boyer, Kristy and Lester, James}, year={2019}, pages={1–1} }
 @inproceedings{park_mott_min_boyer_wiebe_lester_2019, title={Generating educational game levels with multistep deep convolutional generative adversarial networks}, volume={2019-August}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85073108251&partnerID=MN8TOARS}, DOI={10.1109/CIG.2019.8848085}, abstractNote={Educational games offer significant potential for supporting personalized learning in engaging virtual worlds. However, many educational games do not provide adaptive gameplay to meet the needs of individual students. To address this issue, educational games should include game levels that can self-adjust to the specific needs of individual students. However, creating a large number of adaptable game levels requires considerable effort by game developers. A promising solution to this problem is to leverage procedural content generation to automatically generate levels for educational games that incorporate the desired learning objectives. In this paper, we propose a multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education. The model operates in two phases: (1) train a generator with a small set of human-authored example levels and generate a much larger set of synthetic levels to augment the training data for a second generator, and (2) train a second generator using the augmented training data and use it to generate novel educational game levels with enhanced solvability. We evaluate the performance of the model by comparing the novelty and solvability of generated levels between the two generators. Results suggest that the proposed multistep model significantly enhances the solvability of the generated levels with only minor degradation in the novelty of the generated levels.}, booktitle={IEEE Conference on Computatonal Intelligence and Games, CIG}, author={Park, K. and Mott, B.W. and Min, W. and Boyer, K.E. and Wiebe, E.N. and Lester, J.C.}, year={2019} }
 @inproceedings{taylor_min_mott_emerson_smith_wiebe_lester_2019, title={Position: IntelliBlox: A Toolkit for Integrating Block-Based Programming into Game-Based Learning Environments}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078082955&partnerID=MN8TOARS}, DOI={10.1109/BB48857.2019.8941222}, abstractNote={Block-based programming languages reduce the need to learn low-level programming syntax while enabling novice learners to focus on computational thinking skills. Game-based learning environments have been shown to create effective and engaging learning experiences for students in a broad range of educational domains. The fusion of block-based programming with game-based learning offers significant potential to motivate learners to develop computational thinking skills. A key challenge educational game developers face in creating rich, interactive learning experiences that integrate computational thinking activities is the lack of an embeddable block-based programming toolkit. Current block-based programming languages, such as Blockly and Scratch, cannot be easily embedded into industry-standard 3D game engines. This paper presents IntelliBlox, a Blockly-inspired toolkit for the Unity cross-platform game engine that enables learners to create block-based programs within immersive game-based learning environments. Our experience using IntelliBlox suggests that it is an effective toolkit for integrating block-based programming challenges into game-based learning environments.}, booktitle={Proceedings - 2019 IEEE Blocks and Beyond Workshop, B and B 2019}, author={Taylor, S. and Min, W. and Mott, B. and Emerson, A. and Smith, A. and Wiebe, E. and Lester, J.}, year={2019}, pages={55–58} }
 @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} }
 @inproceedings{spain_geden_min_mott_lester_2019, title={Toward computational models of team effectiveness with natural language processing}, volume={2501}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85075911853&partnerID=MN8TOARS}, booktitle={CEUR Workshop Proceedings}, author={Spain, R. and Geden, M. and Min, W. and Mott, B. and Lester, J.}, year={2019}, pages={30–39} }
 @inproceedings{wiggins_kulkarni_min_mott_boyer_wiebe_lester_2018, title={Affect-based early prediction of player mental demand and engagement for educational games}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85070822616&partnerID=MN8TOARS}, booktitle={Proceedings of the 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018}, author={Wiggins, J.B. and Kulkarni, M. and Min, W. and Mott, B. and Boyer, K.E. and Wiebe, E. and Lester, J.}, year={2018}, pages={243–249} }
 @inproceedings{wang_rowe_min_mott_lester_2018, title={High-fidelity simulated players for interactive narrative planning}, volume={2018-July}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85055720882&partnerID=MN8TOARS}, booktitle={IJCAI International Joint Conference on Artificial Intelligence}, author={Wang, P. and Rowe, J. and Min, W. and Mott, B. and Lester, J.}, year={2018}, pages={3884–3890} }
 @inproceedings{akram_min_wiebe_mott_boyer_lester_2018, title={Improving stealth assessment in game-based learning with LSTM-based analytics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85080493724&partnerID=MN8TOARS}, booktitle={Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018}, author={Akram, B. and Min, W. and Wiebe, E. and Mott, B. and Boyer, K.E. and Lester, J.}, year={2018} }
 @inproceedings{wiggins_kulkarni_min_boyer_mott_wiebe_lester_2018, place={Boulder, CO, USA}, title={User Affect and No-Match Dialogue Scenarios: An Analysis of Facial Expression}, ISBN={9781450360760}, url={http://dx.doi.org/10.1145/3279972.3279979}, DOI={10.1145/3279972.3279979}, abstractNote={Recent years have seen significant advances in natural language dialogue management and a growing recognition that multimodality can inform dialogue policies. A key dialogue policy problem is presented by 'no-match' scenarios, in which the dialogue system receives a user utterance for which no matching response is found. This paper reports on a study of the 'no-match' problem in the context of a dialogue agent embedded within a game-based learning environment. We investigate how users' facial expressions exhibited in response to the agent's no-match utterances predict the users' opinion of the agent after the interaction has completed. The results indicate that models incorporating users' facial expressions following no-match utterances are highly predictive of user opinion and significantly outperform baseline models. This work represents a key step toward affect-informed dialogue systems whose policies are informed by users' affective expression.}, booktitle={Proceedings of the 4th International Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction - MA3HMI'18}, publisher={ACM Press}, author={Wiggins, Joseph B. and Kulkarni, Mayank and Min, Wookhee and Boyer, Kristy Elizabeth and Mott, Bradford and Wiebe, Eric and Lester, James}, year={2018}, pages={6–14} }
 @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} }
 @inproceedings{min_mott_rowe_lester_2017, title={Deep LSTM-based goal recognition models for open-world digital games}, volume={WS-17-01 - WS-17-15}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85046086839&partnerID=MN8TOARS}, booktitle={AAAI Workshop - Technical Report}, author={Min, W. and Mott, B. and Rowe, J. and Lester, J.}, year={2017}, pages={851–858} }
 @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} }
 @inproceedings{wang_rowe_min_mott_lester_2017, title={Interactive narrative personalization with deep reinforcement learning}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85031928990&partnerID=MN8TOARS}, booktitle={IJCAI International Joint Conference on Artificial Intelligence}, author={Wang, P. and Rowe, J. and Min, W. and Mott, B. and Lester, J.}, year={2017}, pages={3852–3858} }
 @inproceedings{min_mott_rowe_taylor_wiebe_boyer_lester_2017, title={Multimodal goal recognition in open-world digital games}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85051737443&partnerID=MN8TOARS}, booktitle={Proceedings of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017}, author={Min, W. and Mott, B. and Rowe, J. and Taylor, R. and Wiebe, E. and Boyer, K.E. and Lester, J.}, year={2017}, pages={80–86} }
 @inproceedings{wang_rowe_min_mott_lester_2017, title={Simulating player behavior for data-driven interactive narrative personalization}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85055706729&partnerID=MN8TOARS}, booktitle={Proceedings of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017}, author={Wang, P. and Rowe, J. and Min, W. and Mott, B. and Lester, J.}, year={2017}, pages={255–261} }
 @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} }
 @inproceedings{min_mott_rowe_liu_lester_2016, title={Player goal recognition in open-world digital games with long short-term memory networks}, volume={2016-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85006136250&partnerID=MN8TOARS}, booktitle={IJCAI International Joint Conference on Artificial Intelligence}, author={Min, W. and Mott, B. and Rowe, J. and Liu, B. and Lester, J.}, year={2016}, pages={2590–2596} }
 @inproceedings{min_vail_frankosky_wiggins_boyer_wiebe_pezzullo_mott_lester_2016, title={Predicting dialogue acts for intelligent virtual agents with multimodal student interaction data}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85072280923&partnerID=MN8TOARS}, booktitle={Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016}, author={Min, W. and Vail, A.K. and Frankosky, M.H. and Wiggins, J.B. and Boyer, K.E. and Wiebe, E.N. and Pezzullo, L.G. and Mott, B.W. and Lester, J.C.}, year={2016}, pages={454–459} }
 @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} }
 @inproceedings{min_ha_rowe_mott_lester_2014, title={Deep learning-based goal recognition in open-ended digital games}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84916877257&partnerID=MN8TOARS}, booktitle={Proceedings of the 10th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2014}, author={Min, W. and Ha, E.Y. and Rowe, J. and Mott, B. and Lester, J.}, year={2014}, pages={37–43} }
 @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} }
 @book{min_rowe_mott_lester_2013, title={Personalizing embedded assessment sequences in narrative-centered learning environments: A collaborative filtering approach}, volume={7926 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84880007122&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-39112-5-38}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Min, W. and Rowe, J.P. and Mott, B.W. and Lester, J.C.}, year={2013}, pages={369–378} }
 @book{min_cheong_2009, title={An interactive-content technique based approach to generating personalized advertisement for privacy protection}, volume={5618 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-76249102976&partnerID=MN8TOARS}, DOI={10.1007/978-3-642-02559-4_21}, abstractNote={Personalized contents have been getting more attention from industry and academia due to its effective communicative role in product advertisements. However, there exist potential threats to the customer’s privacy in conventional approaches where a data server containing customer profiles is employed or the customer profiles is required to be sent over the public network. To address this, this paper describes a framework that employs a script-based interactive content technique for privacy protection. We illustrate our approach by a sample scenario.}, number={PART 2}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Min, W.-H. and Cheong, Y.-G.}, year={2009}, pages={185–191} }
 @inproceedings{min_shim_kim_cheong_2008, title={Planning-integrated story graph for interactive narratives}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-72449210908&partnerID=MN8TOARS}, DOI={10.1145/1462014.1462021}, abstractNote={The advances in the interactive contents enable users to have a variety of experiences on diverse devices. In particular, two main approaches have been researched to construct digital interactive contents: a) conditional branch techniques and b) planning techniques. Each approach offers its own benefits; the conditional branch techniques allow the user to create tightly-plotted interactive contents; the planning techniques reduce the author's burden to specify every possible connection between contents considering the user input. As an attempt to combine these advantages provided by each technique, this paper discusses an interactive story structure incorporating the planning technique into the conditional branch techniques. Also, we briefly describe PRISM, a framework capable of creating and playing our story structure. We expect that the author can compose well-woven stories which can respond to a wide range of user interaction.}, booktitle={MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops}, author={Min, W.-H. and Shim, E.-S. and Kim, Y.-J. and Cheong, Y.-G.}, year={2008}, pages={27–32} }
 @book{cheong_kim_min_shim_kim_2008, title={PRISM: A framework for authoring interactive narratives}, volume={5334 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-58449129103&partnerID=MN8TOARS}, DOI={10.1007/978-3-540-89454-4_37}, abstractNote={The advances in computing technologies enable the computer users to create and share their own stories to the community at large. However, it is still regarded as complicated and laborious to author interactive narratives, where a story adapts as the user interacts with it. In authoring interactive narratives, two main approaches—branching graphs and AI planning—have been significantly used to augment interactivity into conventional linear narratives. Although each approach offers its own possibilities and limitations, few efforts have been made to blend these approaches. This paper describes a framework for authoring interactive narratives that employs an adapted branching narrative structure that also uses planning formalism to enable automated association between nodes. We expect that our work is valuable for non-expert users as well as AI researchers in interactive storytelling who need to create a large quantity of story contents for varied endings for a story.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Cheong, Y.-G. and Kim, Y.-J. and Min, W.-H. and Shim, E.-S. and Kim, J.-Y.}, year={2008}, pages={297–308} }
 @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} }