Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

Publication Type:
Conference Proceeding
Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, 2017, pp. 16 - 20
Issue Date:
Filename Description Size
Ontological Learner Profile Identification for Cold Start Problem.pdfPublished version355.4 kB
Adobe PDF
Full metadata record
© 2017 IEEE. Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.
Please use this identifier to cite or link to this item: