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ERIC Number: EJ1115329
Record Type: Journal
Publication Date: 2013
Pages: 10
Abstractor: As Provided
ISSN: EISSN-2157-2100
Properties of the Bayesian Knowledge Tracing Model
van de Sande, Brett
Journal of Educational Data Mining, v5 n2 p1-10 2013
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.
International Educational Data Mining. e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: SBE0836012