1. Discovery of contextual factors using clustering.
- Author
-
Bhaskaran, Subhashini Sailesh
- Subjects
DATA mining ,UNIVERSITIES & colleges ,CONTRADICTION ,ARGUMENT - Abstract
The Knowledge Discovery and Data Mining (KDDM) is a growing field of study argued to be very useful in discovering knowledge hidden in large datasets. KDDM processes are slowly finding application in Higher Educational Institutions (HEIs). While literature shows that KDDM processes enable discovery of knowledge useful to improve performance of organizations, limitations surrounding them contradict this argument. Despite contradictions, KDDM process examples in the literature show that KDDM processes still offer benefits. While extending the usefulness of KDDM processes to support HEIs, challenges were encountered. On the one hand KDDM processes were seen to be promising to support HEIs by discovering hidden knowledge in the educational data and on the other such promises could not be easily realized. One area that prominently stood out as a major challenge was the discovery of course taking patterns in educational datasets associated with contextual information. The dataset pertained to students who had graduated between 2003 and 2014. The attributes used to test the CRISP-DM model were course taking pattern, course difficulty level, optimum CGPA and time-to-degree. When experiments were conducted using CRISP-DM process by applying clustering technique. The results showed that clustering did not produce course taking patterns. The contribution of this research goes to find whether clustering can be used in extracting contextual information. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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