An approach to dealing with incremental concept drift in personalized learning systems

In recent years, personalized learning systems have garnered significant academic research attention in the field of education. In a personalized learning system, learners receive a customized learning style that is tailored to their unique needs, goals, and abilities. Thus, students can achieve their objectives faster than with the traditional method of learning. Rapid advancements in artificial intelligence technologies enable tracking and influencing each student’s learning process. Machine learning algorithms facilitate the determination of students’ learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, in which the models’ performance deteriorates over time due to changes in data distribution. For successful personalization, it is critical for the underlying predictive and classification models to adapt to data distribution changes. In this paper, we propose an approach to address concept drifts in personalized learning systems, and evaluate the approach on the OULAD dataset infused with concept drift. The proposed method comprises training utilizing sequential features extracted automatically.