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

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...

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Bibliographic Details
Published inJournal of educational data mining Vol. 4; no. 1; pp. 144 - 182
Main Authors Kerr, Deirdre, Chung, Gregory K. W. K
Format Journal Article
LanguageEnglish
Published International Educational Data Mining 2012
Subjects
Academic Achievement
Accuracy
Algebra
Data Analysis
Data Collection
Design
Educational Games
Error Patterns
Evidence Based Practice
Grade 6
Grade 7
Grade 8
Learning Strategies
Middle School Students
Multivariate Analysis
Simulation
Video Games
California
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ISSN2157-2100
2157-2100

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Summary: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.
ISSN:2157-2100
2157-2100