Predicting Students' Problem Solving Performance using Support Vector Machine

Young-Jin Lee

《Journal of Data Science》 14卷2期 (2016/04) Pp. 231-243

https://doi.org/10.6339/JDS.201604_14(2).0003

並列摘要

This study investigates whether Support Vector Machine (SVM) can be used to predict the problem solving performance of students in the computer-based learning environment. The SVM models using RBF, linear, polynomial and sigmoid kernels were developed to estimate the probability for middle school students to get mathematics problems correct at their first attempt without using hints available in the computer-based learning environment based on their problem solving performance observed in the past. The SVM models showed better predictions than the standard Bayesian Knowledge Tracing (BKT) model, one of the most widely used prediction models in educational data mining research, in terms of Area Under the receiver operating characteristic Curve (AUC). Four SVM models got AUC values from 0.73 to 0.77, which is approximately 29% improvement, compared to the standard BKT model whose AUC was 0.58.

並列關鍵字

Bayesian Knowledge Tracing (BKT) ； Educational Data Mining ； Log File Analysis ； Support Vector Machine (SVM)