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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2016 Vol.6(1): 71-75 ISSN: 2010-3689
DOI: 10.7763/IJIET.2016.V6.661

Students' Data-Driven Decision Making in HEI: The Explicit Knowledge Involved

Semiu A. Akanmu and Zulikha Jamaludin
Abstract—Due to increase in the volume of students’ data and the limitations of the available data management tools, higher education institutions (HEIs) are experiencing information overload and constrained decision making process. To attend to this, Information Visualization (InfoVis) is suggested as a befitting tool. However, since InfoVis design must be premised on a pre-design stage that outlines the explicit knowledge to be discovered by the HEIs, so as to actualize a functional and befitting InfoVis framework, this study investigates the explicit knowledge through survey questionnaires administered to 32 HEI decision makers. The result shows that relationship between the students’ performance and their program of study is the most prioritized explicit knowledge, among others. Based on the findings, this study elicits a comprehensive data dimensions (attributes) expected of each data instance in the HEI students’ datasets to achieve an appropriate InfoVis framework that will support the discovery of the explicit knowledge. Our future work therefore include designing the appropriate visualization, interaction and visual data mining techniques that will support the explicit knowledge discovery and HEI students’ data-driven decision making types.

Index Terms—Explicit knowledge, HEI students’ data-driven decision making types, InfoVis, knowledge discovery.

The authors are with School of Computing, Universiti Utara Malaysia, Sintok, Malaysia (e-mail: ayobami.sm@gmail.com, zulie@uum.edu.my).

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Cite: Semiu A. Akanmu and Zulikha Jamaludin, "Students' Data-Driven Decision Making in HEI: The Explicit Knowledge Involved," International Journal of Information and Education Technology vol. 6, no. 1, pp. 71-75, 2016.

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