An evolving recommender-based framework for virtual learning communities

Abstract

This article presents a recommender framework to provide personalised suggestions for learners taking introductory undergraduate courses. The framework utilises memory-based collaborative filtering algorithm combined with an imbedded web crawler to update learning material. The process of providing recommendation is divided into four steps: learner model extraction, neighbourhood formation, top-N recommendation presentation, and material update. The framework was implemented and has been successfully tested on real learners taking an introductory mathematics course. The learners of the course were divided into two groups. One of the groups, control group, was taught the material of the course using the traditional face-to-face approach. However, the students in the other group, experimental group, had the advantage to use the framework. The performance of the learners in both groups was tested and the results showed that the learners in the experimental group outperformed their counterparts in the control group.