Join us Tuesday, Sep 22 at 11:00am in Baker Hall 232M:

**Variations of the Additive Factors Models of educational data**

Annie Dong, *Human Computer Interaction Institute, Carnegie Mellon, and University of Santa Barbara, California*

The Additive Factors Model is a logistic regression model that attempts to find the best-fit curve for predicting students’ problem step performance. We use different DataShop datasets spanning different domains in conjunction with pre- and post-test data to explore two variations of the Additive Factors Model. One variation used a log opportunity count in place of the opportunity count parameter in the Additive Factors Model. This effectively allows for bigger performance estimate increases at earlier opportunity counts compared to later opportunity counts. We demonstrate that taking the log opportunity improves the model fit based on AIC/BIC but does not alleviate the correlation between skill intercept and skill slope estimates. The second variation used linear, rather than logistic, regression to predict graded success measures rather than 0-1 first-attempt correctness for each problem step. We developed four distinct ways of quantifying graded success and explore how it improves the reliability of AFM’s parameter estimates as compared to pre/post data.