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.
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