Effective Skill Assessment Using Expectation Maximization in a Multi Network Temporal Bayesian Network

We propose a temporal multi network single skill model for effective assessment and prediction of student skills that is more accurate than multi skill conjunctive models while requiring only a fraction of the computational resources to run. Using the Expectation Maximization algorithm we define steps for effective learning of model parameters over 150,000 responses of real student data that reveal important skill knowledge and learning trends. This skill report is exhibited in the paper. Lastly we focus on how to harness the power of the learned parameters to accurately predict an end of year standardized state math test. Our results of prediction using the multiple single skill network model beat out previous best prediction errors using a single multi skilled model. We believe these results could encourage wider use of these machine learning techniques that can now be effectively run on standard computing machines such as PCs found in school computer labs.