Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis

摘要

The assessment cycle of evidence-centered design (ECD) provides a framework fortreating an educational video game or simulation as an assessment. One of the main stepsin the assessment cycle of ECD is the identification of the key features of studentperformance. While this process is relatively simple for multiple choice tests, whenapplied to log data from educational video games or simulations it becomes one of themost serious bottlenecks facing researchers interested in implementing ECD. In thispaper we examine the utility of cluster analysis as a method of identifying key features ofstudent performance in log data stemming from educational video games or simulations.In our study, cluster analysis was able to consistently identify key features of studentperformance in the form of solution strategies and error patterns across levels, whichcontained few extraneous actions and explained a sufficient amount of the data.