Automatic Domain Model Creation and Improvement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (14th, Paris, France, Jun 29-Jul 2, 2021).

Autor/inn/en	Pavlik, Philip I., Jr.; Eglington, Luke G.; Zhang, Liang
Titel	Automatic Domain Model Creation and Improvement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (14th, Paris, France, Jun 29-Jul 2, 2021).
Quelle	(2021), (5 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Information Retrieval; Knowledge Management; Models; Research Methodology; Inferences; Multivariate Analysis; Factor Analysis; Goodness of Fit + Suchen Sie Ihr Suchwort? Wissensmanagement; Analogiemodell; Research method; Forschungsmethode; Inference; Inferenz; Multivariate Analyse; Faktorenanalyse
Abstract	We describe a data mining pipeline to convert data from educational systems into knowledge component (KC) models. In contrast to other approaches, our approach employs and compares multiple model search methodologies (e.g., sparse factor analysis, covariance clustering) within a single pipeline. In this preliminary work, we describe our approach's results on two datasets when using 2 model search methodologies for inferring item or KCs relations (i.e., implied transfer). The first method uses item covariances which are clustered to determine related KCs, and the second method uses sparse factor analysis to derive the relationship matrix for clustering. We evaluate these methods on data from experimentally controlled practice of statistics items as well as data from the Andes physics system. We explain our plans to upgrade our pipeline to include additional methods of finding item relationships and creating domain models. We discuss advantages of improving the domain model that go beyond model fit, including the fact that models with clustered item KCs result in performance predictions transferring between KCs, enabling the learning system to be more adaptive and better able to track student knowledge. [This paper was published in: "Proceedings of the 14th International Conference on Educational Data Mining (EDM21)," International Educational Data Mining Society, 2021, pp. 672-76 (see ED615472).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01