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This year's challenge
How generally or narrowly do students learn? How quickly or slowly? Will the rate of improvement vary between students? What does it mean for one problem to be similar to another? It might depend on whether the knowledge required for one problem is the same as the knowledge required for another. But is it possible to infer the knowledge requirements of problems directly from student performance data, without human analysis of the tasks?
This year's challenge asks you to predict student performance on mathematical problems from logs of student interaction with Intelligent Tutoring Systems. This task presents interesting technical challenges, has practical importance, and is scientifically interesting.
www.kdd.org/kdd2010/kddcup.shtml
Task description
At the start of the competition, we will provide 5 data sets: 3 development data sets and 2 challenge data sets. Each of the data sets will be divided into a training portion and a test portion. Student performance labels will be withheld for the test portion of the challenge data sets but available for the development data sets. The competition task will be to develop a learning model based on the challenge and/or development data sets, use this algorithm to learn from the training portion of the challenge data sets, and then accurately predict student performance in the test sections. At the end of the competition, the actual winner will be determined based on their model's performance on an unseen portion of the challenge test sets. We will only evaluate each team's last submission of the challenge sets.
Important Dates
- March 15 - Call for participants
- April 1 - Competition begins
- June 1 - Competition ends