Abstract
Identifying the various cognitive processes that learners engage while solving an ill-structured problem online learning environment will help provide improved learning experiences and outcomes. This work aims to build a student model and analyze student behaviors in our technology-enhanced learning environment named Fathom used for teaching-learning of ill-structures problem-solving skills in the context of solving software design. Students’ interactions on the system, captured in log files represent their performance in applying the skills towards understanding the problem as a whole and formulating it into subproblems, generating alternative designs, and selecting the optimal solution. We discuss methods for analyzing student behaviors and linking them to student performance. The approach used is a hidden Markov model methodology that builds students’ behavior models from data collected in the log files.
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Reddy, D., Balasubramaniam, V., Shaikh, S., Trapasia, S. (2022). Student Behavior Models in Ill-Structured Problem-Solving Environment. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_46
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DOI: https://doi.org/10.1007/978-3-031-11644-5_46
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