Authors: Fares Fraij; Ayman Al-Dmour; Rafeeq Al-Hashemi; Ahmed Musa
Addresses: Department of Software Engineering, Al-Hussein Bin Talal University, P.O. Box 20, Ma'an 71111, Jordan. ' Department of Computer Information Systems, Al-Hussein Bin Talal University, P.O. Box 20, Ma'an 71111, Jordan. ' Department of Computer Science, Al-Hussein Bin Talal University, P.O. Box 20, Ma'an 71111, Jordan. ' Department of Computer Engineering, Al-Hussein Bin Talal University, P.O. Box 20, Ma'an 71111, Jordan
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.
Keywords: collaborative filtering; individual differences; pre-test; post-test; Pearson product-moment correlation coefficient; PPMCC; PCC; Karl Pearson; recommender frameworks; undergraduate students; personalised suggestions; personalisation; learners; introductory courses; universities; higher education; memory-based algorithms; collaborative algorithms; filtering algorithms; imbedded web crawlers; learning materials; learner model extraction; neighbourhood formation; top-N recommendations; material updates; mathematics; face-to-face teaching; learner performance; experimental groups; control groups; recommenders; recommender systems; e-communities; electronic communities; internet; world wide web; virtual communities; web based communities; online communities; virtual learning communities.
International Journal of Web Based Communities, 2012 Vol.8 No.3, pp.322 - 332
Published online: 20 Aug 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article