Wheel-spinning is the phenomenon where students, in spite of repeated practice, make no progress towards mastering a skill. Prior research has shown that a considerable number of students can get stuck in the mastery learning cycle--unable to master the skill despite the affordances of the educational software. In such situations, the tutor's promise of 'infinite practice' via mastery learning becomes more a curse than a blessing. Prior research on wheel spinning overlooks two aspects: how much time is spent wheel spinning and the problem of imbalanced data. This work provides an estimate of the amount of time students spend wheel spinning. A first-cut approximation is that 24% of student time in the ASSISTments system is spent wheel spinning. However, the data used to train the wheel spinning model were imbalanced, resulting in a bias in the model's predictions causing it to undercount wheel spinning. We identify this misprediction as an issue for model extrapolation as a general issue within EDM, provide an algebraic workaround to modify the detector's predictions to better accord to reality, and show that students spend approximately 28% of their time wheel spinning in ASSISTments.
: Yan Wang, Yue Gong and Joseph Beck (2016). "How Long Must We Spin Our Wheels? Analysis of Student Time and Classifier Inaccuracy". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.