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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.
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Students' Data-driven Decision Making in HEI: The Explicit Knowledge Involved
Semiu Ayobami Akanmu,
Zulikha Jamaludin
IntroductionThe ever-growing natureever-growing nature of HEI students’ data HEI students’ data has accounted for the experience of information experience of information overloadoverload.
Subsequently , it has constrained decision constrained decision making processes.making processes.
Ware, 2000; Spence, 2007Ware, 2000; Spence, 2007
Introduction
• To attend to this, information information
visualization (InfoVis) visualization (InfoVis) is
arguably the suitable tool that
harnesses its strength in
gaining insight gaining insight from large large
multidimensional datasets multidimensional datasets
and therefore aids decision aids decision
making.making.
Introduction
Introduction
Towards the Explicit KnowledgeInfoVisInfoVis must also be preceded by a study to identify the explicit knowledgeto identify the explicit knowledge expected to be utilized to be utilized by its decision decision makers and administrators.makers and administrators.
This paper, therefore aimed at identifying the explicit knowledge involved in HEIs HEIs students’ data-driven decision making students’ data-driven decision making processes.processes.
Delavari, Phon-Amnuaisuk, and Beikzadeh, 2008; Delavari, Phon-Amnuaisuk, and Beikzadeh, 2008; Lam, Bertini, Isenberg, C. Plaisant, and Carpendale, Lam, Bertini, Isenberg, C. Plaisant, and Carpendale,
20122012
Methods
This study investigates the investigates the
explicit knowledge explicit knowledge through
survey questionnaires survey questionnaires
administered to 32 HEI 32 HEI
decision makers. decision makers.
FindingsThe explicit knowledge explicit knowledge expected to be discovered during the students’ data-students’ data-centered visual exploratory processcentered visual exploratory process is represented as below.
Students’ Performance Students’ Performance (SP), Students’ Students’ EnrolmenEnrolment (SE), Students’ Majoring Students’ Majoring CoursesCourses (SM) and Students’ Health Students’ Health History History (SH)
FindingsThe dimensions (attributes)dimensions (attributes) of each data data instanceinstance of the datasets to be involved in the design framework design framework
Our Work in Progress
Thank You!
