A Survey on Clustering Techniques of Webinars to Support E-Learning Using Data Mining

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

This paper performs a detailed discussion about the clustering techniques being proposed by different researchers on Webinars in such a way to support E-Learning Methods. The growth of information technology has lead the learning methods towards webinar where the learner need not go to the place of the tutor or the institution. In providing efficient learning methods and to provide exact information to the learner, clustering the webinar’s become more important. To solve the problem of clustering webinar’s there are different approaches has been discussed from content based, context based, text based, Speech analysis based, and Ontology based. Each of the approach has their own merits and demerits. Also in the clustering, there are approaches like supervised and unsupervised and hierarchical clustering has been discussed. We explore each of them in detail and perform the analysis of each technique according to their performance in supporting E-Learning. The paper also discusses about different data mining techniques could be adapted for E-Learning in detail.

References

[1].Nikos Manouselis, George Kyrgiazos, Giannis Stoitsis, Exploratory study of multi-criteria recommendation algorithms over technology enhanced learning datasets, Journal of e-Learning and Knowledge Society, Vol.10, No.1, 2014.

[2] Rashmi Rekha Borah,” Slow Learners: Role of Teachers and Guardians in Honing their Hidden Skills”, Int. J. of Educational Planning & Administration, vol. 3, no. 2, pp. 139-143, 2013.

[3] Clark, R, C. & Mayer, R.M.,” E-Learning and the science of instruction”, 3rd edition, San Francisco, CA: Pfeiffer. 2011.

[4] J.willems, “Using Learning styles data to inform e-learning design: A study comparing undergraduates, postgraduates and eeducators”, Australasian J.of. Educational technology, vol.27, no.6, pp.863-880, 2011.

[5] Movafegh Ghadirli, H. and Rastgarpour, M.,, “A Model for an Intelligent and Adaptive Tutor based on Web by Jackson’s Learning Styles Profiler and Expert Systems”, Proc.of the Int. MultiConferece of Engineers and Computer Scientists(IMECS 2012) ,vol 1, 2012.

[6] Al-Khalifa, H. S. and Al-Wabel, A. S. 2005. Aided technological methods for special learning: exploratory study. Available online: https://www.gulfkids.com/pdf/Areej.pdf, 2013.

[7] Anitha, A and Krishnan. “A Dynamic Web Mining Framework for E-Learning Recommendations using Rough Sets and Association Rule Mining'. International Journal of Computer Applications, vol.12, no.11, pp.36-41, 2011.

[8] Kumar, V. and Chadha, A. “An Empirical Study of the Applications of Data Mining Techniques in Higher Education”, Nit’s. Of Advanced Computer Science and Applications, vol. 2, no. 3, pp. 80-84, 2011.

[9] Kangaiammal, A., Silambannan, R., Senthamarai, C., and Srinath, M.,” Student Learning Ability Assessment using Rough Set and Data Mining Approaches’. I.J.Modern Education and Computer Science, vol. 5, pp.1- 11, 2013.

[10] Prema, M and Prakasam, S., “Effectiveness of Data Mining – based Elearning system (DMBELS)”, Int.J.of Computer Applications, vol.66, no.19, pp. 31- 36, 2013.

[11] M. AlAjmi, S. Khan, and A. Zamani, “Using Instructive Data Mining Methods to Revise the Impact of Virtual Classroom in ELearning”, Int. J. of Advanced Science and Technology, vol. 45, pp. 125-134, 2012.

[12] Hung, J., Rice, C., and Saba, A., “An Educational Data Mining Model for Online Teaching and Learning”. J. of Educational Technology Development and Exchange, vol.5, no.2, pp.77-94, 2012.

[13] T. Chellatamilan and R. Suresh, “An eLearning Recommendation System using Association Rule Mining Technique”, European J.of Scientific Research, vol.64, no.2, pp.330- 339, 2011.

[14] Amcenko, J., Sileikiene, I., Lieponiene. “Analysis of E-Exam data using data mining techniques”. Available at: https://isd.ktu.lt/it2011/material/Proceedings/6_ ITTL_3.pdf

[15] Anitha, A &Krishnan., “A Dynamic Web Mining Framework for E-Learning Recommendations using Rough Sets and Association Rule Mining”, International Journal of Computer Applications, vol.12, no.11, pp. 36-41, 2011.

[16] Dominguez, A., Yacef, K., and Curran, J., “data mining for Individualized Hints in eLearning”, 2010.

[17] Nichols and Schedule “Theory for ELearning Educational Technology and SOC, Mvol, 6, no, 2, pp, 1-10, 2003

[18] Romero, C., Ventura, and S. (2010), Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews.

[19] Uribe-Tirado, A., Melgar-Estrada, L.-M., &Bornacelly-Castro, J.-A. (2007). Model learning management System as a tool for information, documentation, and knowledge management by research groups. Professional de la Information.

[20] Silva, D., & Vieira, M. (2002). Using data warehouse and data mining resources for ongoing assessment of distance learning. In Proceedings of the 2002 IEEE Conference on Advanced Learning Technologies Kazan, Russia.

[21] Sison, R., & Shimura, M., (1998). Student Modeling and Machine Learning. International Journal of Artificial Intelligence in Education.

[22] Talavera, L., &Gaudioso E. (2004). Mining student data to characterize similar behavior groups in unstructured collaboration spaces. Workshop on Artificial Intelligence in CSCL. ECAI.

[23] Tang, T.Y., McCalla, G., (2005).Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. International Journal on e-Learning.

[24] Tsai, C. J., Tseng, S. S., & Lin, C. Y. (2001). A Two-Phase Fuzzy Mining and Learning Algorithm for Adaptive. Learning Environment. In ICCS ’01: Proceedings of the H. (2004).

[25] Wu X, Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Systems with Applications 27(3).., (2004).

[26] Bresfelean, V. P., Bresfelean, M., Ghisoiu, N. & Comes, C. A. (2008). "Determining Students’ Academic Failure Profile Founded on Data Mining Methods," Proceedings of the 30th International Conference on Information Technology Interfaces (ITI 2008), Dubrovnik, Croatia, 317–322.

[27] Herzog, S. (2006). "Estimating Student Retention and Degree-Completion Time: Decision Trees and Neural Networks Vis-aVis Regression,” New Directions for Institutional Research, 131, 17–33.

[28] Ma Y., Liu, B., Wong, C. K., Yu, P. S. & Lee, S. M. (2000). "Targeting the Right Students Using Data Mining," Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’00), ACM Press, New York, 457–464

[29] Ramasubramanian, Data mining techniques for teaching result analysis using rough set theory, International Computing and Communication conference, 2008.

[30] Lu-Zhi Ping, Analysis of New Teaching Model for Undergraduates in University Based on the Environment of Information Technology - A Case , for Specialty of Information Management & Information System, Education Technology and Computer science, 2009.

[31] Srisaa.n, Experience from teaching performance analysis of object-oriented systems, Frontier Education 2005.

[32] D Suresh and S Prakasam. Article: The Impact of E-learning system using Rank-based Clustering Algorithm (ESURBCA).International Journal of Computer Applications 83(7):13-18, December 2013.

[33] Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosius, T., & Karakos, A. (2012). A Clustering Methodology of Web Log Data for Learning Management Systems. Educational Technology & Society, 15 (2), 154–167.

[34] Man deep Kaur, Kewal krishan, Cluster Analysis of Behavior of E-learners, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-2, May 2013.

[35] Abdous M, w yen, Predicting Relationship between Online Questions Theme and FinalGrade, 2012.

[36] Jyoti Namdeo, Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts, International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 2, February 2014

[37] Prof. Dr. Aytekin ??MAN, instructional design in education: new model, TOJET: The Turkish Online Journal of Educational Technology – January 2011, volume 10 Issue 1.

[38] Samitha R. Babu, Krutika G. Kulkarni, K. Chandra Sekaran, generic agent based Cloud Computing architecture for E-Learning,ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I , Advances in Intelligent Systems and Computing, Vol. 248, pp 523- 533, 2014.

[39] Krenare Pireva, Petros Kefalas, Dimitris Dranidis, Thanos Hatziapostolou, Anthony Cowling, Cloud e-Learning: A New Challenge for Multi-Agent Systems, Agent and Multi-Agent Systems: Technologies and ApplicationsAdvances in Intelligent Systems and Computing Volume 296, 2014, pp 277- 287.

[40] Bennane, A.: Tutoring and multi-agent systems: Modeling from experiences. Informatics in Education-An International Journal 9(2), 171–184 (2010)

[41] Chatzara, K., Karagiannidis, C., Stamatis, D.: Students attitude and learning effectiveness of emotional agents. In: 2010 IEEE 10th International Conference on Advanced Learning Technologies (ICALT), pp. 558–559. IEEE (2010)

Keywords

Learning, Clustering, Data Mining, Webinar, Semantic Ontology.