The multidimensionality of students' data and the limitations of the currently-used data management tools in higher education institutions (HEIs) have been identified as causes of constrained decision-making process in the domain. This, therefore, necessitates a pre-design study for the HEI students' datafocused InfoVis. The objectives of this study are to identify the prevailing practices of HEI data management, the data analytics methods that are generally employed by HEI data analysts and the comprehensive dimensions that are related to HEI students and adequate for conveying the domain explicit knowledge preferences. A mixed method approach with survey questionnaire and interview as data collection methods, is used. The contributions of this study, among others, are: (i) identification of the pattern and relationship of the domains explicit knowledge preferences; and (ii) the elicitation and description of students' data dimensions. These are expected to form the basis for the choice and implementation of the content delivery techniques in designing the domain-focused InfoVis. Our future works therefore entail developing the HEI InfoVis conceptual framework, designing the HEI students' data-focused InfoVis and conducting its users' experimental evaluation.
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.
The need to evaluate Information Visualization (InfoVis) systems, just as other information systems (IS), cannot be overestimated. In its case, evaluating InfoVis has proved to be more challenging, because many of the previous evaluation studies have been on user interface of IS generally, with few attending to the peculiarities of InfoVis. The few InfoVis evaluations recorded have been mainly on its perceptual function through its interface evaluation, and others on its cognitive support through the knowledge discovery process. Evaluating InfoVis decision support effectiveness has been sparingly attended to. This experience is argued to be caused by insufficient explicit evaluation methods for InfoVis' associated abstract concepts -decision support as an example. This paper uses an unobtrusive research method involving thematic analysis of InfoVis-related theoretical literatures to characterize and categorize the InfoVis evaluation theories. The result presents perceptual, cognitive and decision supports as InfoVis evaluation paradigms. The theoretical characterization posited that these supports are sequential and of dependent phases. Finally, based on the theoretical argument and the findings, an evaluation framework for InfoVis' decision support effectiveness is proposed, and the process of its experimental evaluation is suggested.
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