Discovering Patterns of Wheel-Spinning When Using Intelligent Tutors by Applying k-Means Clustering Algorithm

• In Event: Antecedents and Outcomes of Motivation Within Digital Environments

Abstract

Wheel-spinning is unproductive persistence without achievement of mastery of skills.
Understanding wheel-spinning during the use of intelligent tutoring systems (ITS) is crucial to
help improve productivity and learning. In this study, we used the K-Means clustering algorithm
to probe wheel-spinning learning patterns. Our results showed four types of wheel-spinning
patterns in students using ASSISTments, an online mathematics tutoring system. The findings
indicate a lack of motivation, seriousness, math knowledge, or metacognitive ability can cause
failure in learning math with ITS. Therefore, other learning resources in the ITS should be
provided for additional support to reduce wheel-spinning in the ITS.

Authors

• Wenting Weng, Texas A&M University - College Station

• Seoyeon Park, Texas A&M University - College Station