Title: Data analysis to uncover critical parameters for performance appraisal of students in higher education

Authors: Raju Ranjan; Jayanthi Ranjan

Addresses: Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun - 248007, India ' Institute of Management Technology, Raj Nagar, Ghaziabad - 201001, India

Abstract: Performance of students sometimes gives the most committed, knowledgeable, well-intentioned teacher wondering what is wrong with his/her class or a particular student. The growing demand of information which will provide assistance to decision makers in appraisal of a student's performance is guiding a path towards extensive usage of analytical tools for revealing hidden information. The intelligent information from the data of higher education provides hidden information and pattern from students' data, and thus helps in performance appraisal in the academia which will provide avenues for overall growth of the students in the higher education field. The authors through this paper highlighted the methodology to be adapted in reduction of the various available critical parameters and thus identified the key critical parameters for the performance evaluation of the students in higher education. The authors classified the collected data variables into broad categories and applied the data mining techniques to uncover the critical parameters in higher education.

Keywords: cumulative grade point average; CGPA; data analytics; data mining; artificial neural network; ANN.

DOI: 10.1504/IJNVO.2018.090673

International Journal of Networking and Virtual Organisations, 2018 Vol.18 No.1, pp.30 - 50

Received: 22 Dec 2015
Accepted: 03 Apr 2016
Published online: 27 Mar 2018 *

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