Title
Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery
Abstract
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.
Year
DOI
Venue
2017
10.1109/ICALT.2017.22
2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)
Keywords
Field
DocType
Micro Learning,Open Education Resources,Ontology,Cold Start Problem
Ontology (information science),Ontology,Open learning,World Wide Web,Active learning,Open education,Smart system,Cold start,Computer science,Knowledge management,Mobile device,Multimedia
Conference
ISSN
ISBN
Citations 
2161-3761
978-1-5386-3871-2
1
PageRank 
References 
Authors
0.37
10
6
Name
Order
Citations
PageRank
Geng Sun16110.04
Tingru Cui24014.12
Jun Shen323440.40
Dongming Xu454.94
Ghassan Beydoun545645.98
Shiping Chen6619.02