Serious Game Analytics by Design: Feature Generation and Selection Using Game Telemetry and Game Metrics--Toward Predictive Model Construction

Autor/inn/en	Lu, Wenyi; Griffin, Joe; Sadler, Troy D.; Laffey, James; Goggins, Sean P.
Titel	Serious Game Analytics by Design: Feature Generation and Selection Using Game Telemetry and Game Metrics--Toward Predictive Model Construction
Quelle	In: Journal of Learning Analytics, 10 (2023) 1, S.168-188 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Lu, Wenyi) ORCID (Griffin, Joe) ORCID (Sadler, Troy D.) ORCID (Laffey, James) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Educational Games; Learning Analytics; Design; Game Based Learning; Performance Based Assessment; Predictive Measurement; Middle School Students; Science Instruction; Water; Computer Simulation; Information Science + Suchen Sie Ihr Suchwort? Educational game; Lernspiel; Leistungsermittlung; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Wasser; Computergrafik; Computersimulation; Informationswissenschaft
Abstract	The construction of prediction models reflecting players' learning performance in serious games currently faces various challenges for learning analytics. In this study, we design, implement, and field test a learning analytics system for a serious game, advancing the field by explicitly showing which in-game features correspond to differences in learner performance. We then deploy and test a system that provides instructors with clear signals regarding student learning and progress in the game, which instructors could depend upon for interventions. Within the study, we examined, coded, and filtered a substantial gameplay corpus, determining expertise in the game. Mission HydroSci (MHS) is a serious game that teaches middle-school students water science. Using our logging system, designed and implemented along with game design and development, we captured around 60 in-game features from the gameplay of 373 students who completed Unit 3 of MHS in its first field test. We tested eight hypotheses during the field test and presented this paper's results to participating teachers. Our findings reveal several features with statistical significance that will be critical for creating a validated prediction model. We discuss how this work will help future research establish a framework for designing analytics systems for serious games and advancing gaming design and analytics theory. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01