Prediction of Student Performance in Abacus-Based Calculation Using Matrix Factorization

Abstract

We conducted modeling of student learning status and tasks in abacus-based calculation by utilizing matrix factorization on student-generated learning data. The matrix consisted of performance scores on student-task pairs. We decomposed the raw matrix into two matrices, yielding the distributed representations of each student and each task. Prediction of student performance using those decomposed matrices achieved better results than baseline models that use the student biases and task biases. This suggests matrix factorization successfully extracted the interaction of multiple latent features of each task and each student's learning status in abacus-based calculation.