1 / 3

Knowledge Tracing

Knowledge Tracing. Parameters can be learned with the EM algorithm!. Modeling or measuring learning requires modeling knowledge Knowledge Tracing used to model learning. Parameters (probability of learning) (guess/slip) (prior). P(Skill: 0 → 1). P(Skill: 0 → 1). S. S. S. Latent

beck-head
Download Presentation

Knowledge Tracing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge Tracing Parameters can be learned with the EM algorithm! • Modeling or measuring learning requires modeling knowledge • Knowledge Tracing used to model learning Parameters (probability of learning) (guess/slip) (prior) P(Skill: 0 → 1) P(Skill: 0 → 1) S S S Latent (skill knowledge) (dichotomous) P(correct| Skill = 0) P(incorrect| Skill = 1) Observables (question answers) incorrect correct correct

  2. Assumption of Knowledge Tracing • Knowledge tracing assumes that learning rate is the same between each opportunity • Our model associates the learning rate with the particular problem that was encountered • Learning rate between opportunities are the same regardless of which problem the student saw • Forgetting is always set at 0 Knowledge Tracing 0.12 0.12 Item Effect Model(Pardos, Heffernan 2009) • Learning rates are attributes of specific problems • In implementing the model, learning rates must be associated with their respective problem for all sequence orders (permutations)S. 0.11 0.15

  3. Item Order Model(Pardos, Heffernan 2009) The six sequence permutations are modeled with shared Bayesian parameters Also known as Equivalence classes of CPTs (conditional probability tables ) - Implementation harnesses the power of randomization to help estimate accurate parameters using all response data - Permutation Analysis of Randomized Dichotomous Sequences

More Related