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In collaborative problem solving (CPS), people's actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector Autoregression (mlVAR) to investigate CPS processes. Our data were collected from college students who collaborated in triads via a video-conferencing tool (Zoom) to collaborately engage a physics learning game. Video recordings of their verbal interactions were transcribed, coded using a validated CPS framework, and organized into sequences of 10-second windows. Then, mlVAR was applied to the successful vs. unsuccessful CPS sequences to build temporal models for each. A comparison of the models together with a qualitative analysis of the transcripts revealed six temporal relationships common to both, six unique to successful level attempts, and another eight unique to unsuccessful level attempts only. Generally, for successful outcomes, people were likely to answer clarification questions with reasons and to ask for suggestions according to the current game situation, while for unsuccessful CPS level attempts, people were more likely to struggle with unclear instructions and to respond to inappropriate ideas. Overall, our results suggest that mlVAR is an effective approach for temporal analyses of CPS processes by identifying relationships that go beyond a coding and counting approach. [For the full proceedings, see ED623995.]

What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can reflect short-term comprehension for facts, but it is unknown whether it can measure long-term, deep comprehension. We tracked gaze while 147 participants read long, connected, informative texts and completed assessments of rote (factual) and inference comprehension (connecting ideas) while reading a text, after reading a text, after reading five texts, and after a seven-day delay. Gaze-based student-independent computational models predicted both immediate and long-term rote and inference comprehension with moderate accuracies. Surprisingly, the models were most accurate for comprehension assessed after reading all texts and predicted comprehension even after a week-long delay. This shows that eye movements can provide a lens into the cognitive processes underlying reading comprehension, including inference formation, and the consolidation of information into long-term memory, which has implications for intelligent student interfaces that can automatically detect and repair comprehension in real-time. [For the full proceedings, see ED623995.]

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. [For the full proceedings, see ED607784.]

In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random condition in which the tutor made random yet reasonable decisions. We explored two types of trees (weighted and unweighted) as well as two pruning strategies (pre- and post-pruning). We found that post-pruned weighted trees produced the best results with 529 DT-RL rules. The empirical evaluation was conducted in a classroom study using an existing Intelligent Tutoring System (ITS) named Pyrenees. 153 students were randomly assigned to three conditions. The procedure was the same for all students with domain content and required steps strictly controlled. The only substantive differences between the three conditions were the policy: (Full-RL vs. DT-RL vs. Random). Our result showed that as expected the machine induced policies (Full-RL and DT-RL) are significantly more effective than the random policy; more importantly, no significant difference was found between the Full-RL and DT-RL policies though the number of DT-RL rules is less than 15% of the number of the Full-RL rules and the former group also took significantly less time than the latter. [For the full proceedings, see ED596512.]