Abstract
Many e-learning platforms claim, or desire to help improve students’ self-regulated learning (SRL), but SRL theory models are not easily represented with modern machine learning or A.I. technologies because of their cyclic and undirected nature. We apply SRL inspired features to trace data in order to improve modelling of students’ SRL activities. We demonstrate that these features improve predictive accuracy and validate the value of further research into cyclic modelling techniques for SRL.
Presenters
Andrew SchwabeStudent, PhD, University of St. Andrews Primary, Pennsylvania, United States
Details
Presentation Type
Theme
KEYWORDS
Artificial Intelligence, Self Regulated Learning, E-Learning
