Modeling Data from Collaborative Assessments: Learning in Digital Interactive Social Networks

Autor/inn/en	Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen
Titel	Modeling Data from Collaborative Assessments: Learning in Digital Interactive Social Networks
Quelle	In: Journal of Educational Measurement, 54 (2017) 1, S.85-102 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12134
Schlagwörter	Cognitive Ability; Thinking Skills; Evaluation Methods; Educational Assessment; Field Tests; Test Results; Occupational Aspiration; Academic Aspiration; Learning Processes; Computer Simulation; Simulated Environment; Item Response Theory; Models; Cooperative Learning; Interaction; Social Networks; Data Analysis + Suchen Sie Ihr Suchwort? Denkfähigkeit; Education; assessment; Bewertungssystem; Praxisübung; Berufsneigung; Berufsziel; Learning process; Lernprozess; Computergrafik; Computersimulation; Künstliche Umwelt; Item-Response-Theorie; Analogiemodell; Kooperatives Lernen; Interaktion; Social network; Soziales Netzwerk; Auswertung
Abstract	This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. In this article, ATC21S collaborative assessments focus on the project's "ICT Literacy--Learning in digital networks" learning progression. The article includes a description of the development of the learning progression, as well as examples and the logic behind the instrument construction. Assessments took place in random pairs of students in a demonstration digital environment. Modeling of results employed unidimensional and multidimensional item response models, with and without random effects for groups. The results indicated that, based on this data set, the models that take group into consideration in both the unidimensional and the multidimensional analyses fit better. However, the group-level variances were substantially higher than the individual-level variances. This indicates that a total individual estimate of group plus individual is likely a more informative estimate than individual alone but also that the performances of the pairs dominated the performances of the individuals. Implications are discussed in the results and conclusions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01