Commenced in January 2007
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Edition: International
Paper Count: 31584
Questions Categorization in E-Learning Environment Using Data Mining Technique

Authors: Vilas P. Mahatme, K. K. Bhoyar


Nowadays, education cannot be imagined without digital technologies. It broadens the horizons of teaching learning processes. Several universities are offering online courses. For evaluation purpose, e-examination systems are being widely adopted in academic environments. Multiple-choice tests are extremely popular. Moving away from traditional examinations to e-examination, Moodle as Learning Management Systems (LMS) is being used. Moodle logs every click that students make for attempting and navigational purposes in e-examination. Data mining has been applied in various domains including retail sales, bioinformatics. In recent years, there has been increasing interest in the use of data mining in e-learning environment. It has been applied to discover, extract, and evaluate parameters related to student’s learning performance. The combination of data mining and e-learning is still in its babyhood. Log data generated by the students during online examination can be used to discover knowledge with the help of data mining techniques. In web based applications, number of right and wrong answers of the test result is not sufficient to assess and evaluate the student’s performance. So, assessment techniques must be intelligent enough. If student cannot answer the question asked by the instructor then some easier question can be asked. Otherwise, more difficult question can be post on similar topic. To do so, it is necessary to identify difficulty level of the questions. Proposed work concentrate on the same issue. Data mining techniques in specific clustering is used in this work. This method decide difficulty levels of the question and categories them as tough, easy or moderate and later this will be served to the desire students based on their performance. Proposed experiment categories the question set and also group the students based on their performance in examination. This will help the instructor to guide the students more specifically. In short mined knowledge helps to support, guide, facilitate and enhance learning as a whole.

Keywords: Data mining, e-examination, e-learning, moodle.

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[1] Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, “Advances in knowledge discovery and data mining”, AAAI Press, 1996.
[2] C. Romero, S. Ventura and E. García, “Data mining in course management systems: moodle case study and tutorial”, Computer Education 51(1),2008, pp. 368–384.
[3] F.-J. Liu, B.-J. Shih, “Learning activity based elearning material recommendation system”, Ninth, IEEE International Symposium on Multimedia, 2007, pp. 343–348.
[4] J .Mamcenko, I. Sileikiene, J. Lieponiene, R. kulvietiene, “Analysis of e-exam data using data mining techniques”, accessed on 19th Aug 2015,
[5] Gennaro Costagliola, Vittorio Fuccella, Massimiliano Giordano, and Giuseppe Polese, “Monitoring online tests through data visualization”, IEEE Transactions on Knowledge and Data Engineering”, vol. 21, no. 6, June 2009.
[6] Bernardete Ribeiro and Alberto Cardoso, “Behavior pattern mining during the evaluation phase in an e-learning course”, International Conference on Engineering Education – ICEE 2007”, Coimbra, Portugal September 3 – 7, 2007.
[7] Yair Levy, Michelle M. Ramim “A study of online exams procrastination using data analytics techniques” Interdisciplinary Journal of E-Learning and Learning Objects,vol 8, 2012.
[8] Elmano Ramalho, Cavalcanti, Carlos Eduardo Pires, “Detection and evaluation of cheating on college exams using supervised classification”, Informatics in Education, vol. 11, no. 2, pp.169–190, 2012.
[9] Essam Kosba, Osama Badawy, Passant Sabri, “Intelligent examination system to support teacher’s reflection measurement of student’s guided feedback”, International Conference on the Future of Education.Egypt.
[10] J. Mamčenko, I. Šileikienė, J. Lieponienė and R. Kulvietienė,“ Evaluating the data of an e-examination system using a descriptive model in order to identify hidden patterns in students answers”, The Online Journal on Computer Science and Information Technology, vol. 1, no.2
[11] C. Romero and S. Ventura, “Educational data mining: a review of the state-of-the-art”, IEEE Trans. Syst. Man Cybernet”, C Appl. Rev., 40(6),pp. 601–618, 2010.
[12] A. C. Romero and A. S. Ventura, “Educational data mining: A survey from 1995 to 2005”, Journal of Expert Systems Applications, 33(1), pp. 135-146, 2007.
[13] J. Gamulin, O. Gamulin, “Enhancing laboratory teaching in higher education environment using web-based formative colloquiums”, MIPRO 2011-34thnternational Convention on Information and Communication Technology, Electronics and Microelectronics-Proceedings, art. no. 5967237, pp.1189-1194, 2011.
[14] Moodle, 2013.
[15] R. Xu and D. Wunsch, “Survey of clustering algorithms”, IEEE Transaction Neural Networks, vol. 16, no. 3, 2005, pp. 645– 678.