Analytics on talent search examination data
by Anagha Vaidya; Vyankat Munde; Shailaja Shirwaikar
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 16, No. 1, 2020

Abstract: Learning analytics and educational data mining has greatly supported the process of assessing and improving the quality of education. While learning analytics has a longer development cycle, educational data mining suffers from the inadequacy of data captured through learning processes. The data captured from examination process can be suitably extended to perform some descriptive and predictive analytics. This paper demonstrates the possibility of actionable analytics on the data collected from talent search examination process by adding to it some data pre-processing steps. The analytics provides some insight into the learner's characteristics and demonstrates how analytics on examination data can be a major support for bringing the quality in education field.

Online publication date: Mon, 02-Dec-2019

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