A Rough Set Based Classification Model for Grading in Adaptive E-Assessment
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Assessment of students is an integral part of learning process which is related to learning outcomes. The goal of assessment is the estimation of the knowledge that has been acquired by the students via learning. Adaptive assessment is a form of computer-based assessment that adapts to the students’ ability level. Adaptivity is the key functionality of this assessment, in which the questions are selected intelligently to fit the student's level of knowledge. The motivation of this work is to investigate the techniques for the improvement of student assessment. An adaptive grading methodology was developed to effectively assess the performance of students based on time taken to answer the questions and the grade scored and it has been found that this was performing better than predefined grading in terms of classification accuracy. This work focuses on an innovative approach of using of Rough Set Theory to model the students’ performance in the E-Assessment data and to generate the classification rules for knowledge discovery about the students’ performance in both predefined and adaptive grading. The results have shown that adaptive grading performs better than predefined grading methodology.
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