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

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.