The Grokit competition featured in RMT 25:3, p. 1329. Contestants were asked to predict students' responses based on those students' previous responses. The competition ended on Feb. 29, 2012. Many contestants used Rasch models. The most successful contestant was Steffen Rendle, Social Network Analysis, University of Konstanz. He used a "Factorization Machine" which estimates higher order interactions in very sparse datasets in order to predict new data. It is not known whether the increased complexity of the winning algorithm is matched by an increased utility in its predictions beyond those of a simple Rasch model.
Steffen Rendle was asked on the Kaggle blog: "What was your most important insight into the data?" He replied: "The most important predictors were the student, question and the time taken for answering a question. I also derived a variable that states how often a student has tried to answer a question before. Besides this, I only used the sub-track name and the game type. All other variables didn't help -- at least not in my experiments."
Report on the Kaggle-Grockit Competition ... Rasch Measurement Transactions, 2012, 25:4, 1340
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