Developing a Stealth Assessment System Using a Continuous Conjunctive Model

Autor/inn/en	Yu, Jiaqi; Ma, Wenchao; Moon, Jewoong; Denham, Andre R.
Titel	Developing a Stealth Assessment System Using a Continuous Conjunctive Model
Quelle	In: Journal of Learning Analytics, 9 (2022) 3, S.11-31 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Yu, Jiaqi) ORCID (Ma, Wenchao) ORCID (Moon, Jewoong) ORCID (Denham, Andre R.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Learning Analytics; Student Evaluation; Educational Games; Computer Games; Game Based Learning; Data Collection; Mathematics Education; Models; Mastery Learning; Scaffolding (Teaching Technique); Expertise; Elementary School Students; Alabama + Suchen Sie Ihr Suchwort? Schulnote; Studentische Bewertung; Educational game; Lernspiel; Computer game; Computerspiel; Computerspiele; Data capture; Datensammlung; Mathematische Bildung; Analogiemodell; Expert appraisal
Abstract	Integrating learning analytics in digital game-based learning has gained popularity in recent decades. The interactive nature of educational games creates an ideal environment for learning analytics data collection. However, past research has limited success in producing accessible and effective assessments using game learning analytics. In this study, a mathematics educational game called The Nomads was designed and developed to train learners' adaptive expertise in rational number arithmetic. Players' game log data were captured and fitted to a cognitive diagnostic model (CDM)--CCM (continuous conjunctive model). CCM lends itself well to the complex and dynamic nature of game learning analytics. Unlike traditional CDMs, CCM generates parameters at an attribute level and offers more parsimonious diagnoses using continuous variables. The findings suggest that learners' attribute mastery improved during the gameplay and that learners benefit from using the scaffolds for three of the attributes instructed by the game. This study presents the application of a powerful new tool for game learning analytics. Future studies can benefit from more generalized analytics models and more specified learning attributes and game tasks. (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