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: firstname.lastname@example.org, email@example.com).
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.