Literaturnachweis - Detailanzeige
Autor/in | Levy, Roy |
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Institution | National Center for Research on Evaluation, Standards, and Student Testing |
Titel | Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837 |
Quelle | (2014), (32 Seiten)
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Monographie |
Schlagwörter | Video Games; Educational Games; Bayesian Statistics; Observation; Evaluation Methods; Inferences; Student Evaluation; Diagnostic Tests; Models; Misconceptions; Psychometrics; Performance Based Assessment; Addition; Mathematics Skills; Problem Solving; Grade 6; Grade 7; Grade 8; Markov Processes; Monte Carlo Methods Video game; Videospiel; Videospiele; Educational game; Lernspiel; Beobachtung; Inference; Inferenz; Schulnote; Studentische Bewertung; Diagnostic test; Diagnostischer Test; Analogiemodell; Missverständnis; Psychometry; Psychometrie; Leistungsermittlung; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Problemlösen; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; Markowscher Prozess; Monte-Carlo-Methode |
Abstract | 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. A Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described. (As Provided). |
Anmerkungen | National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |