Predicting Student Performance in an Educational Game Using a Hidden Markov Model

Autor/inn/en	Tadayon, Manie; Pottie, Gregory J.
Titel	Predicting Student Performance in an Educational Game Using a Hidden Markov Model
Quelle	In: IEEE Transactions on Education, 63 (2020) 4, S.299-304 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Tadayon, Manie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0018-9359
DOI	10.1109/TE.2020.2984900
Schlagwörter	Prediction; Performance; Educational Games; Markov Processes; Scores; Video Games; Computation; Mathematics + Suchen Sie Ihr Suchwort? Vorhersage; Achievement; Leistung; Educational game; Lernspiel; Markowscher Prozess; Video game; Videospiel; Videospiele; Mathematik
Abstract	Contributions: Prior studies on education have mostly followed the model of the cross-sectional study, namely, examining the pretest and the posttest scores. This article shows that students' knowledge throughout the intervention can be estimated by time-series analysis using a hidden Markov model (HMM). Background: Analyzing time series and the interaction between the students and the game data can result in valuable information that cannot be gained by only cross-sectional studies of the exams. Research Questions: Can an HMM be used to analyze the educational games? Can an HMM be used to make a prediction of the students' performance? Methodology: The study was conducted on (N=854) students who played the Save Patch game. Students were divided into class 1 and class 2. Class 1 students are those who scored lower in the posttest than class 2 students. The analysis is done by choosing various features of the game as the observations. Findings: The state trajectories can predict the students' performance accurately for both classes 1 and 2. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01