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

Author

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  • 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)

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.

Suggested Citation

  • Geng Sun & Tingru Cui & Ghassan Beydoun & Shiping Chen & Fang Dong & Dongming Xu & Jun Shen, 2017. "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, MDPI, vol. 9(6), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:898-:d:99792
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    References listed on IDEAS

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    1. Jan Hylén & Dirk Van Damme & Fred Mulder & Susan D’Antoni, 2012. "Open Educational Resources: Analysis of Responses to the OECD Country Questionnaire," OECD Education Working Papers 76, OECD Publishing.
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    Cited by:

    1. Bo Jiang & Yanbai He & Rui Chen & Chuanyan Hao & Sijiang Liu & Gangyao Zhang, 2020. "Progressive Teaching Improvement For Small Scale Learning: A Case Study in China," Future Internet, MDPI, vol. 12(8), pages 1-15, August.
    2. Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    3. Yang Chi & Yue Qin & Rui Song & Hao Xu, 2018. "Knowledge Graph in Smart Education: A Case Study of Entrepreneurship Scientific Publication Management," Sustainability, MDPI, vol. 10(4), pages 1-21, March.
    4. Hyunwoo Hwangbo & Yangsok Kim, 2019. "Session-Based Recommender System for Sustainable Digital Marketing," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    5. Thomas Dolmark & Osama Sohaib & Ghassan Beydoun & Kai Wu, 2021. "The Effect of Individual’s Technological Belief and Usage on Their Absorptive Capacity towards Their Learning Behaviour in Learning Environment," Sustainability, MDPI, vol. 13(2), pages 1-17, January.

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