1 source: bruce mclareneducational data mining seminar 2007/08 educational data mining ws 2007/08...
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1Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Educational Data MiningEducational Data MiningWS 2007/08WS 2007/08
Introduction to the SeminarIntroduction to the Seminar
Dr. habil Erica MelisDr. habil Erica MelisDr. Dr. Bruce M. McLarenBruce M. McLaren
Paul LibbrechtPaul Libbrecht
Deutsches Forschungszentrum für Künstliche IntelligenzDeutsches Forschungszentrum für Künstliche Intelligenz
2Source: Bruce McLaren Educational Data Mining Seminar 2007/08
What is Educational Data Mining (EDM)?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?
3Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Uses of Educational Data MiningUses 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
4Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Educational Data Mining Tools & TechniquesEducational Data Mining Tools & TechniquesMachine 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 TechniquesBayesian analysis of data
Language analysis, esp. for collaborative systemsOff-the shelf tools
TagHelper
5Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Seminar ScheduleSeminar Schedule22.10.2007 Introduction - DFKI Bledsoe
29.10.2007 Introduction to Machine Learning - 16.00 DFKIRoom to be decided and published on website
05.11.2007 ActiveMath Presentation and Demo - 16.00 DFKIRoom to be decided and published on website
Work on projects throughout the semesterMeet 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!
6Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Course Requirements - GradingCourse Requirements - GradingKey requirement: Present a paper from the seminar website:
http://www.activemath.org/teaching/eddatamining0708/literature.php
Papers selected during today’s seminar, if you miss the first seminar contact: Dr. Erica Melis ([email protected]) Dr. Bruce McLaren ([email protected]) Paul Libbrecht ([email protected])
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/
7Source: Bruce McLaren Educational Data Mining Seminar 2007/08
Any Questions?Any Questions?