Latent Space Model for Process Data

Autor/inn/en	Chen, Yi; Zhang, Jingru; Yang, Yi; Lee, Young-Sun
Titel	Latent Space Model for Process Data
Quelle	In: Journal of Educational Measurement, 59 (2022) 4, S.517-535 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chen, Yi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12337
Schlagwörter	Data Processing; Social Networks; Statistical Analysis; Educational Assessment + Suchen Sie Ihr Suchwort? Datenverarbeitung; Social network; Soziales Netzwerk; Statistische Analyse; Education; assessment; Bewertungssystem
Abstract	The development of human-computer interactive items in educational assessments provides opportunities to extract useful process information for problem-solving. However, the complex, intensive, and noisy nature of process data makes it challenging to model with the traditional psychometric methods. Social network methods have been applied to visualize and analyze process data. Nonetheless, research about statistical modeling of process information using social network methods is still limited. This article explored the application of the latent space model (LSM) for analyzing process data in educational assessment. The adjacent matrix of transitions between actions was created based on the weighted and directed network of action sequences and related auxiliary information. Then, the adjacent matrix was modeled with LSM to identify the lower-dimensional latent positions of actions. Three applications based on the results from LSM were introduced: action clustering, error analysis, and performance measurement. The simulation study showed that LSM can cluster actions from the same problem-solving strategy and measure students' performance by comparing their action sequences with the optimal strategy. Finally, we analyzed the empirical data from PISA 2012 as a real case scenario to illustrate how to use LSM. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01