Educational Psychology-Empowered Personalized Learning Path Generation Strategy

Abstract

In e-learning, high-quality learning path generation can meet learners’ personalized demands and solve their cognitive disorientation dilemma. However, existing learning path generation schemes still have challenges, such as focusing solely on one aspect of the learner’s characteristics or the structure of learning content, difficulty in describing the variation in a learner’s knowledge level, and a lack of interpretability. To address these issues, in this article, we propose an educational psychology-empowered personalized learning path generation strategy. First, inspired by Brown’s decay theory of immediate memory, we design the decay attentive knowledge tracing approach for assessing a learner’s knowledge level. Then, motivated by Bruner’s cognitive structure learning theory, we present search space optimization for selecting the learning content candidate set. Finally, enlightened by Posner’s conceptual change model, we impose multiple rule constraints on the matching process of the learner’s knowledge level and the learning content in the candidate set, gradually forming the personalized learning path. Experimental results demonstrate the performance of the proposed strategy for guaranteeing the reasonableness of learning content organization and enhancing the learner’s knowledge level. Moreover, the actual utilization of the proposed strategy in higher education course instruction shows its effectiveness in improving learning outcomes, motivation, and engagement.