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

• Published in: International journal of artificial intelligence in education Vol. 23; no. 1-4; pp. 94 - 114
• Main Authors: Sabourin, Jennifer L., Shores, Lucy R., Mott, Bradford W., Lester, James C.
• Format: Journal Article
• Language: English
• Published: New York Springer New York 01.11.2013; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Behavior; Classification; Computer Assisted Instruction; Computer Science; Computers and Education; Educational Environment; Educational Games; Educational Technology; Feedback; Game theory; Games; Goal Orientation; Investigations; Learner Engagement; Learning activities; Learning Strategies; Machine learning; Metacognition; Monitoring; Motivation; Predictor Variables; Problem solving; Regulation; Research Article; School environment; Self Control; Self evaluation; Social Networks; Student Behavior; Student Motivation; Students; Teaching Methods; Tutoring; User Interfaces and Human Computer Interaction; Self-regulated learning; Metacognition; Machine learning
• ISSN: 1560-4292; 1560-4306
• DOI: 10.1007/s40593-013-0004-6

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.