Spectral Bayesian Knowledge Tracing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Falakmasir, Mohammad; Yudelson, Michael; Ritter, Steve; Koedinger, Ken
Institution	International Educational Data Mining Society
Titel	Spectral Bayesian Knowledge Tracing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Bayesian Statistics; Models; Skill Development; Intelligent Tutoring Systems; Mastery Learning; Probability; Accuracy; Validity; Markov Processes; Evaluation Methods + Suchen Sie Ihr Suchwort? Analogiemodell; Kompetenzentwicklung; Qualifikationsentwicklung; Intelligentes Tutorsystem; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Gültigkeit; Markowscher Prozess
Abstract	Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is also treated as a binary variable. While the BKT served the ITS community well, representing both the latent state and the observed performance as binary variables is, nevertheless, a simplification. In addition, BKT as a two-state and two-observation first-order HMM is prone to noise in the data. In this paper, we present work that uses feature compensation and model compensation paradigms in an attempt to conceptualize a more flexible and robust BKT model. Validation of this approach on the KDD Cup 2010 data shows a tangible boost in model accuracy well over the improvements reported in the literature. [This research paper was partially supported by the Andrew Mellon Pre-doctoral Fellowship and extends a project initiated during the Pittsburgh Science of Learning Center's Summer School at Carnegie Mellon University. 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