An Evaluation of Clustering Algorithms for Modeling Game-Based Assessment Work Processes

Abstract

Game-based assessments (GBAs) use game design elements to make assessments more engaging for students and capture response data about work processes. GBA response data are often too complex to plan for every potential response pattern, so some researchers have turned to exploratory cluster analysis to classify students’ work processes. This paper identifies the design elements specific to GBAs and investigates how well k-means, self-organizing maps (SOM), and robust clustering using links (ROCK) clustering algorithms group response patterns in prototypical GBA response data. Results from a simulation study are discussed, and a tutorial is provided with recommendations of general considerations and best practices for analyzing GBA data with clustering algorithms.