Serious Use of a Serious Game for Language Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The WEKA data mining software: an update
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BiLAT: A Game-Based Environment for Practicing Negotiation in a Cultural Context
International Journal of Artificial Intelligence in Education
Early prediction of cognitive tool use in narrative-centered learning environments
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Integrating learning, problem solving, and engagement in narrative-centered learning environments
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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Self-regulated learning behaviors such as goal setting and monitoring have been found to be key to students' success in a broad range of online learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing interest in the intelligent tutoring systems community. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation of 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 that can later be predicted using machine learning techniques. This paper describes the methodology used to classify students and discusses initial analyses demonstrating the different learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these categories early into the student's interaction are presented. These models can be leveraged in future systems to provide adaptive scaffolding of self-regulation behaviors.