Properties of the Bayesian Knowledge Tracing Model

Autor/in	van de Sande, Brett
Titel	Properties of the Bayesian Knowledge Tracing Model
Quelle	In: Journal of Educational Data Mining, 5 (2013) 2, S.1-10 (10 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Bayesian Statistics; Markov Processes; Student Evaluation; Probability; Computation; Learning Processes; Prediction; Maximum Likelihood Statistics; Equations (Mathematics); Mathematical Models + Suchen Sie Ihr Suchwort? Markowscher Prozess; Schulnote; Studentische Bewertung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Learning process; Lernprozess; Vorhersage; Equations; Mathematics; Gleichungslehre; Mathematical model; Mathematisches Modell
Abstract	Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model parameters. We present an analytical solution to this model and show that it is, in fact, a function of three parameters and has the functional form of an exponential. The second form is the Knowledge Tracing "algorithm" which uses student performance at each opportunity to apply a skill to update the conditional probability that the student has learned that skill. We use a fixed point analysis to study solutions of this model and find a range of parameters where it has the desired behavior. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01