KDD Cup 2010 Workshop

July 25, 2010, 9am-12pm

Held as part of 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010) in Washington, DC July 25-28

This workshop will include a discussion of the 2010 KDD Cup competition, and the winning teams will present their work.

This year's competition asked participants to predict student performance on mathematical problems from logs of student interaction with Intelligent Tutoring Systems. The competition addressed questions of both scientific and practical importance. Improved models could be saving millions of hours of students' time (and effort) in learning algebra. These models should both increase achievement levels and reduce time needed to learn.

For more information, see the KDD Cup 2010 website.

Workshop Schedule

Time Duration
Intro 9am 5 min
Invited Talk
Cognitive Tutor: Modeling to improve mathematics education
Steve Ritter, Co-Founder & Chief Scientist
Tristan Nixon, Research Programmer
Carnegie Learning, Inc.
9:05am 30 min
Organizers' Talk
John Stamper and Alexandru Niculescu-Mizil
9:35am 20 min
Winning Talk: 1st Place Overall
Feature engineering and classifier ensembling for KDD Cup 2010
National Taiwan University team
(workshop paper)
9:55am 20 min
Break 10:15am 20 min
Winning Talk: 3rd Place Overall
Collaborative Filtering Applied to Educational Data Mining
Andreas Töscher, BigChaos @ KDD team
(workshop paper)
10:35am 20 min
Winning Talk: 2nd Place Student Track
Using HMMs and bagged decision trees to leverage rich features of user and skill
Zach A. Pardos
(workshop paper)
10:55am 20 min
Winning Talk: 3rd Place Student Track
SCUT Data Mining team
(workshop paper)
11:15am 20 min
EDM Future Discussion
Ken Koedinger
11:35am 20 min
Closing 11:55am 5 min

Additional papers submitted to the workshop

Poonam Gandhi & Varun Aggarwal
Ensemble Hybrid Logit Model
Tri Kurniawan Wijaya & Philips Kokoh Prasetyo
Knowledge Tracing with Stochastic Method
Yasser Tabandeh & Ashkan Sami
Classification of Tutor System Logs with High Categorical Features
Rafael Perez Mendoza, Neil Rubens & Toshio Okamoto
Hierarchical Aggregation Prediction Method
Kun Liu & Yan Xing
A Lightweight Solution to the Educational Data Mining Challenge