The Misidentified Identifiability Problem of Bayesian Knowledge Tracing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (10th, Wuhan, China, Jun 25-27, 2017).

Autor/inn/en	Doroudi, Shayan; Brunskill, Emma
Titel	The Misidentified Identifiability Problem of Bayesian Knowledge Tracing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (10th, Wuhan, China, Jun 25-27, 2017).
Quelle	(2017), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Bayesian Statistics; Research Problems; Statistical Analysis; Models; Learning; Prediction; Markov Processes; Identification; Probability; Goodness of Fit + Suchen Sie Ihr Suchwort? Forschungskritik; Statistische Analyse; Analogiemodell; Lernen; Vorhersage; Markowscher Prozess; Identifikation; Identifizierung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different parameters can give rise to the same predictions about student performance. We show that the problem they pointed out was not an identifiability problem, and using an existing result from the identifiability of hidden Markov models, we show that under mild conditions on the parameters, BKT is actually identifiable. In the second part of the paper, we discuss a problem that has been conflated with identifiability, but which actually does arise when fitting BKT models, "semantic model degeneracy"--the model parameters that best fit the data are inconsistent with the conceptual assumptions underlying BKT. We give some intuition for why semantic model degeneracy may arise by showing that BKT models fit to data generated from alternative models of student learning can have semantically degenerate parameters. Finally, we discuss the potential implications of these insights. [This article was published in: Proceedings of the 10th International Conference on Educational Data Mining, Wuhan, China, June 25-27, 2017 (p143-149).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01