Predicting Early and Often: Predictive Student Modeling for Block-Based Programming Environments.
Andrew Emerson, Andy Smith, Cody R. Smith, Fernando J. Rodríguez, Wookhee Min, Eric Wiebe, Bradford Mott, Kristy Elizabeth Boyer, James C. Lester · BearWorks (Missouri State University) · 2019
Recent years have seen a growing interest in block-based programming environments for computer science education. While these environments hold significant potential for novice programmers, they lack the adaptive support necessary to accommodate students exhibiting a wide range of initial capabilities and dispositions toward computing. A promising approach to addressing this problem is introducing adaptive feedback. This work investigates a key capability for adaptive support: training student models that predict student success in block-based programming activities for novice programmers. The predictive student models utilize four categories of features: prior performance, hint usage, activity progress, and interface interaction. In addition to evaluating the accuracy of these models for multiple block-based programming activities, we also investigate how quickly the models converge to accurate prediction, and we evaluate the additive value of each of the four categories of features. Results show that the predictive models are able to predict whether a student will successfully complete an exercise with high accuracy, as well as converge on this prediction early in the sequence of student interactions.