Automated Search for Logistic Knowledge Tracing Models [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (16th, Bengaluru, India, Jul, 2023).

Autor/inn/en	Pavlik, Philip I., Jr.; Eglington, Luke G.
Titel	Automated Search for Logistic Knowledge Tracing Models [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (16th, Bengaluru, India, Jul, 2023).
Quelle	(2023), (11 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Students; Models; Regression (Statistics); Alternative Assessment; Data Analysis; Cloze Procedure; Automation; Computer Software; Item Response Theory + Suchen Sie Ihr Suchwort? Student; Schüler; Schülerin; Studentin; Analogiemodell; Regression; Regressionsanalyse; Auswertung; Lückentext; Item-Response-Theorie
Abstract	This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for the regression-based models (e.g., step-wise selection or LASSO), we are unaware of their application to student modeling. Such automatic methods of model creation offer the possibility of better student models with either reduced com-plexity or better fit, in addition to relieving experts from the bur-den of searching for better models by hand with possible human error. Our new functions are now part of the preexisting R pack-age LKT. We explain our search methods with two datasets demonstrating the advantages of using the tool with stepwise re-gression and regularization (LASSO) methods to aid in feature selection. For the stepwise method using BIC, the models are simpler (due to the BIC penalty for parameters) than alternatives like BestLR with little lack of fit. For the LASSO method, the models can be made simpler due to the fitting procedure involving a regularization parameter that penalizes large absolute coefficient values. However, LASSO also offers the possibility of highly complex models with exceptional fit. [This paper was published in: "Proceedings of the 16th International Conference on Educational Data Mining," edited by M. Feng et. al., International Educational Data Mining Society, 2023, pp.17-27.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01