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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Adaptive E-Assessment; Degree of Toughness (DT); Adaptive Grading; Rough Sets; Classifier Accuracy; Discriminant Analysis

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Barla, M, Bielikova, M, Ezzeddinne, A.B, Kramar, T, Simko, M. Vozar, O. On the Impact of Adaptive Test Question Selection for Learning Efficiency, Computers & Education,Vol.55,No.2,pp.846-857, 2010.

Baylari.A, Montazer, G.A. Design a Personalized E-learning System based on Item Response Theory and Artificial Neural Network Approach, Expert Systems with Applications, Vol.36, No.4,pp.8013-8021, 2009.

Lilley M, Barker T, Britton C, The Development and Evaluation of a Software Prototype for Computer-Adaptive Testing, Computers & Education, Vol.43, No.1, pp.109-123, 2004.

Han J, Kamber M, Data Mining Concepts and Techniques (Morgan Kaufmann Publishers, 2001).

Al-Radaideh Q, Al-Shawakfa E, Al-Najjar M, Mining Student Data using Decision Trees, The 2006 International Arab Conference on Information Technology (ACIT'2006) 2006.

Daubie M, Levecq P, Meskens, N, A Comparison of Rough Sets and Recursive Partitioning Induction Approaches: An Application to Commercial Loans, International Transactions in Operational Research, pp. 681–694, 2002.

CaoY, Liu S, Zhang L, Qin J, Wang J, Tang K, Prediction of Protein Structural Class with Rough Sets, BMC Bioinformatics, pp.7-20, 2006.

Revett K, A Rough Set Based Classifier for Primary Biliary Cirrhosis, International Conference on Computers as a Tool, pp.1128-1131, 2005.

Ruzgar B, Ruzgar N.S, Loan Payment Prediction using Rough Sets, American Conference on Applied Mathematics (MATH '08), Recent Advances on Applied Mathematics, Harvard, Massachusetts, USA, pp.124-129, 2008.

Kim S, Jun S, Han S, Effective Reasoning of Learning Styles using Rough Set Theory in e-Learning, Proceedings of the 5th WSEAS International Conference on E-ACTIVITIES, Venice, Italy, pp.79-83, 2006.

Mitra, S., Mitra, M. and Chaudhuri, B.B. An Approach to Rough Set Based Disease Inference Engine for ECG Classification, Rough Sets and Current Trends in Computing, Proceedings of 5th International Conference, RSCRC, pp.400-406, 2006.

Perzyk M,. Soroczynski A, Comparative Study of Decision Trees and Rough Sets Theory as Knowledge Extraction Tools for Design and Control of Industrial Processes, World Academy of Science, Engineering and Technology, pp.84-90, 2010.

Melissa J.J, Bakar A.A, Rough Set and Decision Tree Knowledge Model for Student’s Curriculum Performance Action Model, International Conference on Computational Science and Information Management, Vol.1, pp.41-50, 2012.

David J.M, Balakrishnan, K, Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children, International Journal of Computer Applications,Vol.9 No.11, pp.7–14, 2010.

Geetha V , Surendiran B, Nadarajan R, Nandakumar G.S, An Adaptive E-Assessment Grading (AEAG) Model for Performance Evaluation, International Journal on Computational Sciences & Applications (IJCSA) Vol.2, No.4, pp.25-38.2012.


Geetha, V., Chandrakala, D., Nadarajan, R., Dass, C.K., A bayesian classification approach for handling uncertainty in adaptive E-assessment, (2013) International Review on Computers and Software (IRECOS), 8 (4), pp. 1045-105.

Geetha V, Amritha Menon A, Dass C.K, Nadarajan R,

E-Assessment Performance Classification using Rough Set Theory, International Conference on Mathematical Modeling and Applied Computing, (MMAC12), Coimbatore Institute of Technology, 2012.

Aleksander Øhrn, ROSETTA Technical Reference Manual, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway, 2001.

Aleksander Øhrn, Discernibility and Rough Sets in Medicine: Tools and Applications, PhD Thesis, Norwegian University of Science and Technology, Trondheim, Norway, 1999.

Nguyen, H. S, and Skowron,A, Quantization of Real-valued Attributes, In the Proceedings of Second International Joint Conference on Information Sciences , pp.34-37,1995.


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