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Mining Educational Data for Academic Accreditation: Aligning Assessment with Outcomes

Author

Listed:
  • Mohammed Hussain

    (Zayed University)

  • Mohamed Al-Mourad

    (Zayed University)

  • Sujith Mathew

    (Zayed University)

  • Abdullah Hussein

    (University of Sharjah)

Abstract

Institutions in higher education generate terabytes of data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. The analysis of the data is used to evaluate the institution against a standard set by an accreditation body, for the purpose of the academic accreditation of higher education programs. Therefore, the framework reduces human involvement in accreditation. The paper provides the detailed design of the process of aligning assessment with student learning outcomes.

Suggested Citation

  • Mohammed Hussain & Mohamed Al-Mourad & Sujith Mathew & Abdullah Hussein, 2017. "Mining Educational Data for Academic Accreditation: Aligning Assessment with Outcomes," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(1), pages 51-60, March.
    • Handle: RePEc:spr:gjofsm:v:18:y:2017:i:1:d:10.1007_s40171-016-0143-3
    DOI: 10.1007/s40171-016-0143-3
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    Cited by:

    1. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.

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