A Bio-Inspired Method for Personalized Learning Path Recommendation Problem

Abstract

The recommendation of personalized learning paths is recognized as one of the most challenging aspects in the field of e-learning. In the existing literature, numerous approaches have been proposed to identify appropriate learning paths for e-learners, taking into consideration multiple perspectives. However, the current state of research lacks a unified framework that effectively integrates the most vital parameters associated with the learner, learning object (LO), and domain knowledge to generate optimal learning paths. To address this challenge, a novel bio-inspired approach is proposed for solving the personalized learning path problem. In this method, we initially incorporate the learner, LO and domain knowledge models into a unified mathematical model. Then an enhanced ant colony optimization algorithm is utilized to determine the optimal personalized learning paths for learners. To investigate the effectiveness of the proposed method, we performed several computational experiments based on six simulation datasets. The results indicate that the proposed method surpasses other competing methods in terms of performance and robustness, showcasing its superior effectiveness.