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

Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu and Jun Shen
Additional contact information
Geng Sun: School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
Tingru Cui: School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
Ghassan Beydoun: School of Systems, Management and Leadership, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
Shiping Chen: CSIRO Data61, Marsfield, NSW 2122, Australia
Fang Dong: School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Dongming Xu: UQ Business School, The University of Queensland, Brisbane, QLD 4072, Australia
Jun Shen: School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia

Sustainability, 2017, vol. 9, issue 6, 1-21

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.

Keywords: adaptive learning; micro open learning; educational data mining and learning analytics; cold start problem (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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