When and How to Update Online Analytical Models for Predicting Students Performance?

Abstract

One of the main concerns in online learning environments is the identification of students with learning difficulties. Conventionally, analytical models trained offline on pre-prepared datasets are used to predict student performance. However, as learning data become progressively available over time, this learning method is no longer sufficient in real-world applications. Nowadays, incremental learning strategies are increasingly applied to update online analytical models by re-training them on newly received data. Various online incremental learning approaches have been proposed to overcome different issues such as catastrophic forgetting and concept drift. However, no approach addresses the question of when to update the model and how to determine whether the new data provide important information that the model should learn. In this paper, we propose a method for determining when an online classifier that predicts student performance and receives a real-time data stream, should be updated. In addition, we use a typical approach that maintains balanced old and new data examples to re-train the model when necessary. As a proof of concept, we applied our method on real data of k-12 learners enrolled in an online physics-chemistry module.