XGBoost To Enhance Learner Performance Prediction

Abstract

The huge amount of data generated by an Intelligent Tutoring System becomes useful when analyzed in an appropriate way to provide significant insights about learners, especially his or her performance. Performance data retrieved from historical interactions is the main engine for learner performance prediction, where the likelihood of the learner answering correctly future questions is calculated. Modeling learner performance can provide significant insights into individual students to promote successful learning and maximize educational achievement. This study aims to enhance the learner performance prediction of some logistic regression-based models, namely Item Response Theory, Performance Factor Analysis, and DAS3H using XGBoost, including an empirical comparison of eight real-world datasets, containing performance log data collected from different online intelligent tutoring systems, involving the first time a new dataset from Moodle Morocco. The results have demonstrated that the XGBoost has enhanced PFA predictive performance on seven datasets with an AUC of up 0.88 and improved the DAS3H AUC on the ASSISTment17 dataset while conserving almost the same predictive results for Item Response Theory on some datasets.