Journal article Open Access

Decision Tree Modeling of Wheel- Spinning and Productive Persistence in Skill Builders

Kai, Shimin; Almeda, Victoria; Baker, Ryan S.; Heffernan, Cristina; Heffernan, Neil

Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills, show that not all persistence is uniformly beneficial for learning. For this reason, it becomes increasingly pertinent to identify the key differences between unproductive and productive persistence toward informing interventions in computer-based learning environments. In this study, we use a classification model to distinguish between productive persistence and wheel-spinning in ASSISTments, an online math learning platform. Our results indicate that there are two types of students who wheel-spin: first, students who do not request any hints in at least one problem but request more than one bottom-out hint across any 8 problems in the problem set; second, students who never request two or more bottom out hints across any 8 problems, do not request any hints in at least one problem, but who engage in relatively short delays between solving problems of the same skill. These findings suggest that encouraging students to both engage in spaced practice and use bottom-out hints sparingly is likely helpful for reducing their wheel-spinning and improving learning. These findings also provide insight on when students are struggling and how to make students' persistence more productive.

Erratum for Kai, S., Almeda, M.V., Baker, R.S., Heffernan, C., Heffernan, N. (2018) Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders. Journal of Educational Data Mining, 10 (1), 36-71. In the original published version of the article, it was stated that student-level cross-validation was used. However, upon later re-analysis by another member of our laboratory, Yeyu Wang, it was determined that cross-validation had been inadvertently conducted at the level of student-skill pairs. When the overall model was re-validated using student-level cross-validation, the overall model's goodness dropped by just under 0.05 (AUC ROC), from 0.684 to 0.636.
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