Personalized Learning Path Recommendation Using Hierarchical Successive Fitness Ant Colony Algorithm

In the field of personalized learning path recommendation, although evolutionary computation is one of the commonly used recommendation algorithms, it often fails to comprehensively consider the following three key factors: the multidimensional information characteristics of the learners and learning resources (LRs) during global optimization, and the topological relationship between knowledge points and the dynamic changes in the learners' knowledge levels during the iterative process. How to ensure global optimization while taking into account the local dynamic changes is an urgent problem. To address this problem, a personalized learning path recommendation method (FHSFACO) based on a hierarchical successive fitness ant colony algorithm (HSFACO) is proposed. First, a global optimization fitness function (F) is constructed based on the multidimensional information characteristics of learners and LRs; then, HSFACO is proposed based on the hierarchical management of knowledge points and LRs, which utilizes the topology map of knowledge points to select the successive knowledge points and selects the successive LRs based on pheromones, and at the same time combines with the item response theory to dynamically adapt the knowledge level of the learner; Next, the fitness function F is integrated with the HSFACO algorithm to achieve the recommendation of personalized learning path. Finally, the FHSFACO method is compared with the recommendation methods based on the original ant colony algorithm (ACO), genetic algorithm (GA) and binary particle swarm optimization (BPSO) in terms of path fitness, optimization process and time efficiency. The experimental results show that the FHSFACO method has significant effectiveness in personalized learning path recommendation.