Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Mirjana Ivanović,Boban Vesin,Zoran Budimac,Aleksandra Klašnja-Milićević

doi:10.1007/s10489-017-1051-8

Abstract

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.

Highlights

“Web 2.0” has made new significant concepts for education on the Internet
Based on the current trend, present in the recent period, we investigate the process of integration recommender systems and collaborative tagging techniques into e-learning systems
The research presented in this paper focuses on the proper selection of collaborative tagging techniques that could increase motivation of learners and their comprehension of the learning content

Summary

Introduction

“Web 2.0” has made new significant concepts for education on the Internet. Collaborative tagging, as a fragment of Web 2.0, is an important tool for classification dynamic content [1]. This paper analyzes possible integration of recommender systems and collaborative tagging in an intelligent web-based programming tutoring system, named Protus (PRogramming TUtoring System) that has possibilities to take into account pedagogical aspects of the learner. The research presented in this paper focuses on the proper selection of collaborative tagging techniques that could increase motivation of learners and their comprehension of the learning content. We incorporate tag-based recommendation into a learning approach implemented in Protus Such approach allows the system to quickly identify the most suitable supplementary material for learners and present it to them. It can be expected that personalized recommendations in the system will be consistent with the learner’s interests and previously acquired knowledge This approach should simplify collaboration and interaction between learners

Results

Discussion

Conclusion