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Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments
ARTICLE

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IJAIE Volume 23, Number 1, ISSN 1560-4292

Abstract

Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation into self-regulated learning in a game-based learning environment. Evidence of goal setting and monitoring behaviors is examined through students' text-based responses to update their "status" in an in-game social network. Students are then classified into SRL-use categories. This article describes the methodology used to classify students and discusses analyses demonstrating the learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these classes early in students' interaction are presented.

Citation

Sabourin, J.L., Shores, L.R., Mott, B.W. & Lester, J.C. (2013). Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments. International Journal of Artificial Intelligence in Education, 23(1), 94-114. Retrieved April 21, 2021 from .

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