Performance Comparison of Tree-Based Algorithms for Wheel-Spinning Behavior Prediction

• In Event: Computers and Internet Applications in Education SIG Paper Session

Abstract

Detecting wheel-spinning behaviors of students who interact with an Intelligent Tutoring System (ITS) is important for generating pertinent and effective feedback and developing more enriching learning experiences. This analysis compares decision tree and bagged tree models of student productive persistence (i.e., mastering a skill) using the ASSISTment 2009-2010 dataset for n = 4,217 middle-school students in the United States to predict whether a student is wheel-spinning. Although both models yielded high predictive accuracy, bagged trees significantly outperformed decision trees. Results show that (1) a tree-based model is effective at accurately predicting wheel-spinning and (2) students are taking more than the average amount of attempts to master a skill.

Authors

• Lydia Marion González-Esparza, University of Alberta

• Hao-Yue Jin, University of Alberta

• Chang Lu, Shanghai Jiao Tong University

• Maria Cutumisu, McGill University