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ERIC Number: EJ1115399
Record Type: Journal
Publication Date: 2012
Pages: 39
Abstractor: As Provided
ISSN: EISSN-2157-2100
Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis
Kerr, Deirdre; Chung, Gregory K. W. K.
Journal of Educational Data Mining, v4 n1 p144-182 2012
The assessment cycle of "evidence-centered design" (ECD) provides a framework for treating an educational video game or simulation as an assessment. One of the main steps in the assessment cycle of ECD is the identification of the key features of student performance. While this process is relatively simple for multiple choice tests, when applied to log data from educational video games or simulations it becomes one of the most serious bottlenecks facing researchers interested in implementing ECD. In this paper we examine the utility of cluster analysis as a method of identifying key features of student performance in log data stemming from educational video games or simulations. In our study, cluster analysis was able to consistently identify key features of student performance in the form of solution strategies and error patterns across levels, which contained few extraneous actions and explained a sufficient amount of the data.
International Educational Data Mining. e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Middle Schools; Secondary Education; Junior High Schools; Grade 6; Intermediate Grades; Elementary Education; Grade 7; Grade 8
Audience: N/A
Language: English
Sponsor: National Center for Education Research (ED); Institute of Education Sciences (ED)
Authoring Institution: N/A
Identifiers - Location: California
IES Funded: Yes
Grant or Contract Numbers: R305C080015