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

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dc.contributor.advisorStapleton, Lauraen_US
dc.contributor.advisorSweet, Tracyen_US
dc.contributor.authorFossey, William Austinen_US
dc.contributor.departmentHuman Developmenten_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-01-23T06:42:01Z
dc.date.available2018-01-23T06:42:01Z
dc.date.issued2017en_US
dc.description.abstractGame-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.en_US
dc.identifierhttps://doi.org/10.13016/M2599Z34K
dc.identifier.urihttp://hdl.handle.net/1903/20363
dc.language.isoenen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pqcontrolledQuantitative psychologyen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledClustering Algorithmsen_US
dc.subject.pquncontrolledEvidence-Centered Designen_US
dc.subject.pquncontrolledGame-Based Assessmentsen_US
dc.subject.pquncontrolledK-Meansen_US
dc.subject.pquncontrolledRobust Clustering Using Linksen_US
dc.subject.pquncontrolledSelf-Organizing Mapsen_US
dc.titleAn Evaluation of Clustering Algorithms for Modeling Game-Based Assessment Work Processesen_US
dc.typeDissertationen_US

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