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2009

Using Dirichlet priors to improve model parameter plausibility

9 years 5 months ago
Using Dirichlet priors to improve model parameter plausibility
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its parameters. Second, a commonly used student modeling technique, knowledge tracing, suffers from having multiple sets of parameters providing equally good model fits. Furthermore, common methods for estimating parameters, including conjugate gradient descent and expectation maximization, suffer from finding local maxima that are heavily dependent on their starting values. We propose a technique that estimates Dirichlet priors directly from the data, and show that using those priors produces model parameters that provide a more plausible picture of student knowledge. Although plausibility is difficult to quantify, we employed external measure...
Dovan Rai, Yue Gong, Joseph Beck
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EDM
Authors Dovan Rai, Yue Gong, Joseph Beck
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