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Workshop: Introduction to Educational Data Mining Yiling Zeng & Shirley Alexander Educational Data Mining (EDM) is an emerging area of research concerned with the use of data mining techniques in education in order to predict student performance, provide recommendations on optimal student interventions, and develop models of student behaviour. It is one of a number of contemporary approaches to learning analytics which relies on the development of data mining techniques. This workshop will commence with a brief overview of the evolution of the Intelligent Tutoring Systems of the 1980s which attempted to build student models to today’s Educational Data Mining techniques. A variety of techniques on the prediction of student performance, student modelling, student grouping, social network analysis and feedback and recommendation providing will also be introduced in this workshop. A case study on a recently-developed educational data mining system, i-Educator, which is able to predict students’ academic risk levels with potential reasons will be used to illustrate the use of various EDM techniques. The analysis of iEducator on particular subjects with high failure rates will be used as examples to demonstrate the use of the following EDM techniques: Contrast pattern mining; Voting forest (A forest of decision trees) Ensemble Methods for integrating different models Workshop outcomes: participants will be able to Articulate the evolution of techniques involved in the modelling of student behaviour student Compare a range of basic educational data mining techniques Develop a plan to model student behaviour Workshop approach: Presentation and discussion of Intelligent Tutoring Systems – successes and failures Case Study – An analysis of an educational data mining system which models students’ behaviours on ”killer subjects” to demonstrate the relevant EDM techniques Interactions with exercises based on the concepts and algorithms introduced. Relevant references and resources: Cristobal Romero and Sebastian Ventura: Educational Data Mining: A Review of the State-of-the-Art. Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews. 40(6) pp: 601 – 618, 2010. Ryan S.J.D. Baker and Kalina Yacef: The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), pp:3-17, 2009. International Conference on Educational Data Mining. http://www.educationaldatamining.org/proceedings.html Journal of Educational Data Mining. http://www.educationaldatamining.org/JEDM/index.php IEEE Task Force on Educational Data Mining. http://datamining.it.uts.edu.au/edd/ About the facilitators Yiling Zeng is a postdoc research associate at Advanced Analytics Institute, UTS. His research includes educational data mining, machine learning and text mining. He has worked on several student analytics projects, including the UTS Vice Chancellor’s Learning and Teaching Grant Phase I and Phase II. Shirley Alexander is Deputy Vice-Chancellor (Teaching,Learning & Equity) at UTS. She has undergraduate and postgraduate qualifications in Statistics and Artificial Intelligence and is leading the university-wide project for UTS to become a “data intensive university”.