Author:
Dade Nurjanah
Affiliation:
Telkom University, Indonesia
Keyword(s):
Recommender Systems, Collaborative Filtering, Content-based Filtering, Good Learners, Similar Learners, Adaptation Engine, Adaptive Learning Systems.
Related
Ontology
Subjects/Areas/Topics:
Collaborative Learning
;
Computer-Supported Education
;
e-Learning
;
e-Learning Hardware and Software
;
Social Context and Learning Environments
;
Web 2.0 and Social Computing for Learning and Knowledge Sharing
Abstract:
Classic challenges in adaptive learning systems are about performing adaptive navigation that recommends a topic or concept to be learned next and learning materials relevant to the topic. Both recommendations have to meet active learners’ needs. As adaptive navigation problems have been solved using artificial intelligence techniques, learning material recommendation problems can be solved using recommender techniques that have been successfully applied to other problems. Until recently there have been a number of techniques that come with certain advantages and disadvantages. This paper proposes a new technique for recommending learning materials that combine content-based filtering and collaborative filtering based on the similarity between learners and learners’ competence. It aims to diminish the drawback of classic collaborative filtering, which is based on the similarities between learners and does not consider learners’ competence. It also diminishes problems arising from col
laborative filtering based on good learners’ competence, which potentially produces recommended objects that do not meet the learners’ condition. The results of a recent experiment show that the proposed technique performs well, as indicated by the MAE score of 0.96 for a rating scale of 1 to 10.
(More)