@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}, 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} }
 @inbook{zambrano_barany_ocumpaugh_nasiar_hutt_goslen_rowe_lester_wiebe_mott_2023, title={Cracking the Code of Learning Gains: Using Ordered Network Analysis to Understand the Influence of Prior Knowledge}, url={https://doi.org/10.1007/978-3-031-47014-1_2}, DOI={10.1007/978-3-031-47014-1_2}, abstractNote={Prior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design.}, author={Zambrano, Andres Felipe and Barany, Amanda and Ocumpaugh, Jaclyn and Nasiar, Nidhi and Hutt, Stephen and Goslen, Alex and Rowe, Jonathan and Lester, James and Wiebe, Eric and Mott, Bradford}, year={2023} }
 @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} }