@article{wiedbusch_dever_goslen_carpenter_marano_smith_azevedo_2024, title={Contextualizing Plans: Aligning Students Goals and Plans During Game-Based Inquiry Science Learning}, volume={14723}, ISBN={["978-3-031-61684-6"]}, ISSN={["1611-3349"]}, url={https://doi.org/10.1007/978-3-031-61685-3_9}, DOI={10.1007/978-3-031-61685-3_9}, journal={LEARNING AND COLLABORATION TECHNOLOGIES, PT II, LCT 2024}, author={Wiedbusch, Megan and Dever, Daryn and Goslen, Alex and Carpenter, Dan and Marano, Cameron and Smith, Kevin and Azevedo, Roger}, year={2024}, pages={113–128} }
 @article{goslen_taub_carpenter_azevedo_rowe_lester_2024, title={Leveraging Student Planning in Game-Based Learning Environments for Self-Regulated Learning Analytics}, volume={9}, ISSN={["1939-2176"]}, url={https://doi.org/10.1037/edu0000901}, DOI={10.1037/edu0000901}, journal={JOURNAL OF EDUCATIONAL PSYCHOLOGY}, author={Goslen, Alex and Taub, Michelle and Carpenter, Dan and Azevedo, Roger and Rowe, Jonathan and Lester, James}, year={2024}, month={Sep} }
 @article{goslen_carpenter_rowe_henderson_azevedo_lester_2022, title={Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning}, volume={13355}, ISBN={["978-3-031-11643-8"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11644-5_7}, abstractNote={Goal setting and planning are integral components of self-regulated learning. Many students struggle to set meaningful goals and build relevant plans. Adaptive learning environments show significant potential for scaffolding students' goal setting and planning processes. An important requirement for such scaffolding is the ability to perform student plan recognition, which involves recognizing students' goals and plans based upon the observations of their problem-solving actions. We introduce a novel plan recognition framework that leverages trace log data from student interactions within a game-based learning environment called CRYSTAL ISLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. We formalize student plan recognition in terms of two complementary tasks: (1) classifying students' selected problem-solving goals, and (2) classifying the sequences of actions that students indicate will achieve their goals. Utilizing trace log data from 144 middle school students' interactions with CRYSTAL ISLAND, we evaluate a range of machine learning models for student goal and plan recognition. All machine learning-based techniques outperform the majority baseline, with LSTMs outperforming other models for goal recognition and naive Bayes performing best for plan recognition. Results show the potential for automatically recognizing students' problem-solving goals and plans in game-based learning environments, which has implications for providing adaptive support for student self-regulated learning.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I}, author={Goslen, Alex and Carpenter, Dan and Rowe, Jonathan P. and Henderson, Nathan and Azevedo, Roger and Lester, James}, year={2022}, pages={78–89} }
 @article{fahid_acosta_lee_carpenter_mott_bae_saleh_brush_glazewski_hmelo-silver_et al._2022, title={Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_38}, abstractNote={Collaborative game-based learning environments offer significant promise for creating effective and engaging group learning experiences. These environments enable small groups of students to work together toward a common goal by sharing information, asking questions, and constructing explanations. However, students periodically disengage from the learning process, which negatively affects their learning, and the impacts are more severe in collaborative learning environments as disengagement can propagate, affecting participation across the group. Here, we introduce a multimodal behavioral disengagement detection framework that uses facial expression analysis in conjunction with natural language analyses of group chat. We evaluate the framework with students interacting with a collaborative game-based learning environment for middle school science education. The multimodal behavioral disengagement detection framework integrating both facial expression and group chat modalities achieves higher levels of predictive accuracy than those of baseline unimodal models.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Acosta, Halim and Lee, Seung and Carpenter, Dan and Mott, Bradford and Bae, Haesol and Saleh, Asmalina and Brush, Thomas and Glazewski, Krista and Hmelo-Silver, Cindy E. and et al.}, year={2022}, pages={218–221} }
 @article{cloude_carpenter_dever_azevedo_lester_2021, title={Game-Based Learning Analytics for Supporting Adolescents' Reflection}, volume={8}, ISSN={["1929-7750"]}, DOI={10.18608/jla.2021.7371}, abstractNote={Reflection is critical for adolescents’ problem solving and learning in game-based learning environments (GBLEs). Yet challenges exist in the literature because most studies lack a theoretical perspective and clear operational definition to inform how and when reflection should be scaffolded during game-based learning. In this paper, we address these issues by studying the quantity and quality of 120 adolescents’ written reflections and their relation to their learning and problem solving with Crystal Island, a GBLE. Specifically, we (1) define reflection and how it relates to skill and knowledge acquisition; (2) review studies examining reflection and its relation to problem solving and learning with emerging technologies; and (3) provide direction for building reflection prompts into GBLEs that are aligned with the learning goals built into the learning session (e.g., learn about microbiology versus successfully solve a problem) to maximize adolescents’ reflection, learning, and performance. Overall, our findings emphasize how important it is to examine not only the quantity of reflection but also the depth of written reflection as it relates to specific learning goals. We discuss the implications of using game-learning analytics to guide instructional decision making in the classroom.}, number={2}, journal={JOURNAL OF LEARNING ANALYTICS}, author={Cloude, Elizabeth B. and Carpenter, Dan and Dever, Daryn A. and Azevedo, Roger and Lester, James}, year={2021}, pages={51–72} }
 @article{carpenter_cloude_rowe_azevedo_lester_2021, title={Investigating Student Reflection during Game-Based Learning in Middle Grades Science}, DOI={10.1145/3448139.3448166}, abstractNote={Reflection plays a critical role in learning by encouraging students to contemplate their knowledge and previous learning experiences to inform their future actions and higher-order thinking, such as reasoning and problem solving. Reflection is particularly important in inquiry-driven learning scenarios where students have the freedom to set goals and regulate their own learning. However, despite the importance of reflection in learning, there are significant theoretical, methodological, and analytical challenges posed by measuring, modeling, and supporting reflection. This paper presents results from a classroom study to investigate middle-school students’ reflection during inquiry-driven learning with Crystal Island, a game-based learning environment for middle-school microbiology. To collect evidence of reflection during game-based learning, we used embedded reflection prompts to elicit written reflections during students’ interactions with Crystal Island. Results from analysis of data from 105 students highlight relationships between features of students’ reflections and learning outcomes related to both science content knowledge and problem solving. We consider implications for building adaptive support in game-based learning environments to foster deep reflection and enhance learning, and we identify key features in students’ problem-solving actions and reflections that are predictive of reflection depth. These findings present a foundation for providing adaptive support for reflection during game-based learning.}, journal={LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE}, author={Carpenter, Dan and Cloude, Elizabeth and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2021}, pages={280–291} }
 @article{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} }