TY - JOUR
T1 - Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments
AU - Levy, Roy
N1 - Funding Information:
Conflict of interest disclosures: The author signed a form for disclosure of potential conflicts of interest. The author reported a financial or other conflicts of interest in relation to the work described. See below for details on funding. Ethical principles: The author affirms having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data. Funding: This work was supported by Grant R305C080015 from the Institute of Education Sciences, U.S. Department of Education, awarded to the Center for Advanced Technology in Schools. Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Acknowledgments: An earlier version of the work reported here appeared as CRESST report 837 (Levy, 2014). The author would like to thank Deirdre Kerr for invaluable insights regarding interpretations of student activities that form the basis for this work, and Mark Hansen and Ron Dietel for comments on earlier versions that have yielded improvements to this paper. The author would also like to thank Associate Editor Sarah Depaoli and two anonymous reviewers for comments that have yielded improvements to this paper. The ideas and opinions expressed herein are those of the author alone, and endorsement by the author’s institution, the Center for Advanced Technology in Schools, the National Center for Education Research, the Institute of Education Sciences, or the U.S. Department of Education is not intended and should not be inferred.
Publisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.
PY - 2019/11/2
Y1 - 2019/11/2
N2 - Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.
AB - Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.
KW - Dynamic Bayesian network
KW - diagnostic assessment
KW - game-based assessment
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U2 - 10.1080/00273171.2019.1590794
DO - 10.1080/00273171.2019.1590794
M3 - Article
C2 - 30942094
AN - SCOPUS:85063880588
VL - 54
SP - 771
EP - 794
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
SN - 0027-3171
IS - 6
ER -