1. 1 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Educational Data Mining WS 2007/08 Introduction to the Seminar Dr. habil Erica Melis Dr. Bruce M. McLaren Paul Libbrecht Deutsches Forschungszentrum für Künstliche Intelligenz

2. 2 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 What is Educational Data Mining (EDM)?  Making good use of the raw data collected by e-Learning and educational technology systems  Motivated by:  Proliferation of data from many Internet-based educational systems  Base conclusions and development on real data rather than conjecture and intuition  Use educational data to, for example, improve systems, evaluate student behavior, support teachers  Interactive Learning Environments: intelligent tutoring systems, collaborative systems, open inquiry systems  Scaling up - Possibility for large-scale and longitudinal analysis  How are students learning from and reacting to educational technologies?

3. 3 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Uses of Educational Data Mining  Find common errors committed or help requests made by students, so that subsequent versions of educational technology can better address them  Student modelling  Learn how to create adaptive systems that change their approach based on different learning styles  Discover ways that students “game” the system, i.e., students that do not seriously try to learn but rather just try to get through the technology, and how to react to this  Provide ways for teachers to analyze -- and react to -- student efforts

4. 4 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Educational Data Mining Tools & Techniques  Machine Learning  Many techniques available -- and have been largely prepackaged, e.g.,  Decision Trees  Support Vector Machines  Boosting algorithms  Off-the shelf tools  WEKA (A flightless bird, found in New Zealand)  YALE (Yet Another Learning Environment)  Statistical Techniques  Bayesian analysis of data  Language analysis, esp. for collaborative systems  Off-the shelf tools  TagHelper

5. 5 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Seminar Schedule  22.10.2007 Introduction - DFKI Bledsoe  29.10.2007 Introduction to Machine Learning - 16.00 DFKI  Room to be decided and published on website  05.11.2007 ActiveMath Presentation and Demo - 16.00 DFKI  Room to be decided and published on website  Work on projects throughout the semester  Meet with your advisor at least twice  Work on your e-Portfolio  Martin Homik will explain shortly …  Presentation of student projects  Selected dates: Thursday Feb 28; Friday, Feb 29  If there are any conflicts with these dates, send email to Erica, Bruce & Paul very soon!

6. 6 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Course Requirements - Grading  Key requirement: Present a paper from the seminar website:  http://www.activemath.org/teaching/eddatamining0708/literature.php http://www.activemath.org/teaching/eddatamining0708/literature.php  Papers selected during today’s seminar, if you miss the first seminar contact:  Dr. Erica Melis (Erica.Melis@dfki.de)Erica.Melis@dfki.de  Dr. Bruce McLaren (Bruce.McLaren@dfki.de)  Paul Libbrecht (paul@activemath.org)  Read not only this paper, but important referenced and related papers  Meet at least twice with your advisor (advisors listed next to each paper on the website)  Send first version of slides to your advisor at least 2 weeks before presentations  Present the paper at the final seminar meetings  Attend the two introductory lectures, plus all student presentations  Participate in lecture discussions  Participate in individual ePortfolios - Martin Homik will explain  http://edm.activemath.org/ http://edm.activemath.org/

7. 7 Source: Bruce McLarenEducational Data Mining Seminar 2007/08 Any Questions?