Contextual Markup and Mining in Digital Games for Science Learning: Connecting Player Behaviors to Learning Goals

Autor/inn/en	Kinnebrew John S.; Killingsworth, Stephen S.; Clark, Douglas B.; Biswas, Gautam; Sengupta, Pratim; Minstrell, James; Martinez-Garza, Mario; Krinks, Kara
Titel	Contextual Markup and Mining in Digital Games for Science Learning: Connecting Player Behaviors to Learning Goals
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017) 1, S.93-103 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2016.2521372
Schlagwörter	Computer Games; Educational Games; Data Analysis; Science Education; Metadata; Learning; Student Behavior; Science Achievement; Student Improvement; Middle School Students; Physics; Pretests Posttests; Science Tests; Grade 8; Achievement Gains; Affordances + Suchen Sie Ihr Suchwort? Computer game; Computerspiel; Computerspiele; Educational game; Lernspiel; Auswertung; Naturwissenschaftliche Bildung; Metadaten; Lernen; Student behaviour; Schülerverhalten; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Physik; School year 08; 8. Schuljahr; Schuljahr 08; Achievement gain; Leistungssteigerung
Abstract	Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment. Therefore, a critical step forward involves developing rich representations for analyzing gameplay data. This paper leverages data mining techniques to model learning and performance, using a metadata markup language that relates game actions to concepts relevant to specific game contexts. We discuss results from a classroom study and identify potential relationships between students' planning/prediction behaviors observed across game levels and improvement on formal assessments. The results have implications for scaffolding specific activities, that include physics learning during gameplay, solution planning and effect prediction. Overall, the approach underscores the value of our contextualized approach to gameplay markup to facilitate data mining and discovery. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01