Identifying Learning Trajectories in an Educational Video Game

Educational video games and simulations hold great potential as measurement tools to assess student levels of understanding, identify effective instructional techniques, and pinpoint moments of learning because they record all actions taken in the course of solving each problem rather than just the answers given. However, extracting meaningful information from the log data produced by educational video games and simulations is notoriously difficult. We extract meaningful information from the log data by first utilizing a logging technique that results in a far more easily analyzed dataset. We then identify different learning trajectories from the log data, determine the varying effects of the trajectories on learning, and outline an approach to automating the process.