The performance of some machine learning approaches and a rich context model in student answer prediction

Abstract

Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners’ interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the student’s learning style, study schedule, knowledge, and performance. Quizzing might be used to help to create individualized/personalized spaced repetition algorithm in order to improve long-term retention of knowledge and provide efficient learning in online learning platforms. Current spaced repetition algorithms have pre-defined repetition rules and parameters that might not be a good fit for students’ different learning styles in online platforms. This study uses different machine learning models and a rich context model to analyze quizzing and reading records from e-learning platform called Hypocampus in order to get some insights into the relevant features to predict learning outcome (quiz answers). By knowing the answer correctness, a learning system might be able to recommend personalized repetitive schedule for questions with maximizing long-term memory retention. Study results show that question difficulty level and incorrectly answered previous questions are useful features to predict the correctness of student’s answer. The gradient-boosted tree and XGBoost models are best in predicting the correctness of the student’s answer before answering a quiz. Additionally, some non-linear relationship was found between the reading learning material behavior in the platform and quiz performance that brings added value to the accuracy for all used models.