Optimizing Partial Credit Algorithms to Predict Student Performance

As adaptive tutoring systems grow increasingly popular for the completion of classwork and homework, it is crucial to assess the manner in which students are scored within these platforms. The majority of systems, including ASSISTments, return the binary correctness of a student's first attempt at solving each problem. Yet for many teachers, partial credit is a valuable practice when common wrong answers, especially in the presence of effort, deserve acknowledgement. We present a grid search to analyze 441 partial credit models within ASSISTments in an attempt to optimize per unit penalization weights for hints and attempts. For each model, algorithmically determined partial credit scores are used to bin problem performance, using partial credit to predict binary correctness on the next question. An optimal range for penalization is discussed and limitations are considered. [For complete proceedings, see ED560503.]