Personalized recommendation for web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from an access database through an ant colony model. However, critical limitations arose when considering its applications in real world situations. In this paper, the aim is to improve the model by devising more efficient algorithms that requires a reasonable number of learners and training cycles to find satisfying good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.