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Abstract: In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student learning processes is often challenging because of the information heterogeneity and temporal dynamics. Various inverse reinforcement learning (IRL) algorithms have been successfully employed in many domains for inducing policies from the trajectories and recently has been applied for analyzing students’ temporal logs to identify their domain knowledge patterns. IRL was originally designed to model the data by assuming that all trajectories have a single pattern or strategy. Due to the heterogeneity among students, their strategies can vary greatly and the design of traditional IRL may lead to suboptimal performance. In this paper, we applied a novel expectation-maximization IRL (EM-IRL) to extract heterogeneous learning strategies from sequential data collected from three simulation environments and real-world longitudinal students’ logs. Experiments on simulation environments showed that EM-IRL can successfully identify different policies from the heterogeneous sequences with different strategies. Furthermore, experimental results from our educational dataset showed that EM-IRL can be used to obtain different student subtypes: a “learning-oriented” subtype who learned the material as much as possible regardless of the time in that they spent significantly more time than the other two subtypes and learned significantly; an“efficient-oriented”subtype who learned efficiently in that they not only learned significantly but also spent less time than the first subtype; a “no learning” subtype who spent less amount of time than first subtype and failed to learn. 

The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cog-nitive and metacognitive self-regulated learning processes as they learned about the circulatory system withMetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scoresof emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted theaccuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted theaccuracy of taking notes, a cognitive learning strategy. These results have implications for understanding thebeneficial role of negative emotions during learning with advanced learning technologies. Future directionsinclude providing students with feedback about the benefits of both positive and negative emotions duringlearning and how to regulate specific emotions to ensure the most effective learning experience with advancedlearning technologies 

Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning. 

The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with Crystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern mining and differential sequence mining on 64 undergraduate participants’ hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer sequences of testing items overall, and significantly lower instance support values of the PartiallyRelevant--Relevant to Relevant--Relevant and PartiallyRelevant--PartiallyRelevant to Relevant--Partially Relevant sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.