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Pattern Mining in E-Learning

Subrata Sahana, Saurabh Mittal, Sarthak Jauhari

Abstract


Group study is necessary in learning. Our aim is to distinguish the huge data provided into different categories based on unique patterns. These patterns will be generated according to students expertise. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. Clustering is used in order to separate data into different groups. This whole idea leads to provide a better methodology in e-learning as an individual can perform online discussions as it include chat rooms , performance based on stronger and weaker groups can also be identified and by the use of sequence tool the admin can also identify the subjects which require more detail study material. For this whole concept sequence by sequence(SBS) algorithm is defined and sequential tool is designed based on different patterns.

Keywords


Pattern Mining, Clustering, e-learning, Tracker

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References


Educational Data Mining, http://www.educationaldatamining.org, 2008.

C. Romero, S. Ventura, C.d. Castro, W. Hall, and N.H. Ng, ―Using Genetic Algorithms for Data Mining in Web-Based Educational Hypermedia Systems,‖ Proc. Workshop Adaptive Systems for Web-Based Education, 2002.

A. Merceron and K. Yacef, ―Clustering Students to Help Evaluate Learning,‖ Technology Enhanced Learning, J.-P. Courtiat, C. Davarakis, and T. Villemur, eds., vol. 171, pp. 31-42, Springer, 2005.

Educational Data Mining Events, http://www.educationaldata mining.org/events.html, 2008.

R. Mazza and V. Dimitrova, ―CourseVis: Externalising Student Information to Facilitate Instructors in Distance Learning,‖ Proc. 11th Int’l Conf. Artificial Intelligence in Education (AIED), 2003.

J. Kay, P. Reimann, and K. Yacef, ―Mirroring of Group Activity to Support Learning as Participation,‖ Proc. 13th Int’l Conf. Artificial Intelligence in Education (AIED), 2007.

T. Erickson, C. Halverson, W.A. Kellogg, M. Laff, and T. Wolf, ―Social Translucence: Clustering using K-mean algorithm,‖ Comm. ACM, vol. 45, pp. 40-44, 2002.

T. Erickson and W.A. Kellogg, ―Social Translucence: An Approach to Designing Systems That Support Social Processes,‖ ACM Trans. Computer-Human Interaction, vol. 7, pp. 59-83, 2000.

XP—Extreme Programming, www.extremeprogramming.org, 2007.

TRAC, http://trac.edgewall.org/, 2007.

J. Kay, P. Reimann, and K. Yacef, ―Visualisations for Team Learning: Small Teams Working on Long-Term Projects,‖ Proc. Int’l Conf. Computer-Supported Collaborative Learning (CSCL), 2007.

P.-N. Tan, M. Steinback, and V. Kumar, Introduction to Data Mining. Pearson Addison Wesley, 2006.

I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.

J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Datal, and M.-C. Hsu, ―Freespan: Frequent Pattern-Projected Sequential Pattern Mining,‖ Proc. Sixth Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2000.

J. Pei, B. Mortazavi-Asl, H. Punto, Q. Chen, U. Dayal, and M.-C. Hsu, ―PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,‖ Proc. 17th Int’l Conf. Data Eng. (ICDE), 2001.


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