Personalized Learning Systems for Computer Science Students : Analyzing and Predicting Learning Behaviors Using Programming Error Data

The integration of technology in education has become indispensable in acquiring new skills, knowledge, and competencies. This paper addresses the issue of analyzing and predicting the learning behavior of Computer Science students. Specifically, we present a dataset of compiler errors made by students during the first semester of an Introduction to Programming course where they learn the C programming language. We approach the problem of predicting the number of student errors as a missing data imputation problem, utilizing several prediction methods including Singular Value Decomposition, Polynomial Regression via Latent Tensor Reconstruction, Neural Network-based method, and Gradient Boosting. Our experimental results demonstrate high accuracy in predicting student learning behaviors over time, which can be leveraged to enhance personalized learning for individual students.