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Sustainability 2017, 9(6), 898; doi:10.3390/su9060898

Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

1
School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
2
School of Systems, Management and Leadership, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
3
CSIRO Data61, Marsfield, NSW 2122, Australia
4
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
5
UQ Business School, The University of Queensland, Brisbane, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Laurence T. Yang
Received: 5 April 2017 / Revised: 12 May 2017 / Accepted: 19 May 2017 / Published: 26 May 2017
(This article belongs to the Special Issue Smart X for Sustainability)
View Full-Text   |   Download PDF [1162 KB, uploaded 26 May 2017]   |  

Abstract

Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs. In this paper, we focus on constructing a knowledge base to support the decision-making process of MLaaS. MLaas is built using a top-down approach. A conceptual graph-based ontology construction is first developed. An educational data mining and learning analytic strategy is then proposed for the data level. The learning resource adaptation still requires learners’ historical information. To compensate for the absence of this information initially (aka ‘cold start’), we set up a predictive ontology-based mechanism. As the first resource is delivered to the beginning of a learner’s learning journey, the micro OER recommendation is also optimized using a tailored heuristic. View Full-Text
Keywords: adaptive learning; micro open learning; educational data mining and learning analytics; cold start problem adaptive learning; micro open learning; educational data mining and learning analytics; cold start problem
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Sun, G.; Cui, T.; Beydoun, G.; Chen, S.; Dong, F.; Xu, D.; Shen, J. Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem. Sustainability 2017, 9, 898.

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