On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus
Institution	International Educational Data Mining Society
Titel	On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Knowledge Level; Item Response Theory; Prediction; Comparative Analysis; Models; Performance; Regression (Statistics); Data Analysis; Probability; Simulation; Performance Factors; Error Patterns + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Wissensbasis; Item-Response-Theorie; Vorhersage; Analogiemodell; Achievement; Leistung; Regression; Regressionsanalyse; Auswertung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Simulation program; Simulationsprogramm; Leistungsindikator; Fehlertyp
Abstract	Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge Tracing (LFKT) and Feature Aware Student knowledge Tracing (FAST) exhibit state of the art prediction accuracy. However, only few studies have analyzed the characteristics of these different models. In this paper, we therefore evaluate and compare properties of the models using synthetic data sets. We sample from a combined student model that encompasses all four models. Based on the true parameters of the data generating process, we assess model performance characteristics for over 66'000 parameter configurations and identify best and worst case performance. Using regression we analyze the influence of different sampling parameters on the performance of the models and study their robustness under different model assumption violations. [For complete proceedings, see ED560503.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01