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A big data analytic framework for investigating streaming educational data

Conference Paper


Abstract


  • Last decade has witnessed the dramatic expansion of online usergenerated content. Making full use of this data to discover behaviour patterns has become an increasingly appealing research topic. In this pilot study, a big data analytic framework is proposed, particularly taking streaming data from students' activity on their laptop usage as an illustrative example. Three modules are implemented to harvest raw streaming records, storage heterogeneous data, and apply the fuzzy representation and rule-mining algorithm for a modelling purpose. The efficiency of the proposed framework is then evaluated using a nationwide streaming dataset. The exploratory simulation of results demonstrates the flexibility and applicability of the proposed framework for processing complex streaming data, and revealing patterns from digital engagement which be used to inform decision makers.

Publication Date


  • 2017

Citation


  • Yang, J., Ma, J., Howard, S. K., Ciao, M. & Srikhanta, R. (2017). A big data analytic framework for investigating streaming educational data. Australasian Computer Science Week 2017 (pp. 1-4). New York, United States: Association for Computing Machinery.

Scopus Eid


  • 2-s2.0-85014916340

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • New York, United States

Abstract


  • Last decade has witnessed the dramatic expansion of online usergenerated content. Making full use of this data to discover behaviour patterns has become an increasingly appealing research topic. In this pilot study, a big data analytic framework is proposed, particularly taking streaming data from students' activity on their laptop usage as an illustrative example. Three modules are implemented to harvest raw streaming records, storage heterogeneous data, and apply the fuzzy representation and rule-mining algorithm for a modelling purpose. The efficiency of the proposed framework is then evaluated using a nationwide streaming dataset. The exploratory simulation of results demonstrates the flexibility and applicability of the proposed framework for processing complex streaming data, and revealing patterns from digital engagement which be used to inform decision makers.

Publication Date


  • 2017

Citation


  • Yang, J., Ma, J., Howard, S. K., Ciao, M. & Srikhanta, R. (2017). A big data analytic framework for investigating streaming educational data. Australasian Computer Science Week 2017 (pp. 1-4). New York, United States: Association for Computing Machinery.

Scopus Eid


  • 2-s2.0-85014916340

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • New York, United States