.png){kind=icon}
.png){kind=icon}
Return
Fuzzy Bayesian Knowledge Tracing
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2021.3083177.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Online education promotes the sharing of learning resources. Knowledge tracing (KT) is aimed at tracking the cognition function of students according to their performance on various exercises at different times and has attracted considerable attention. Existing KT models primarily use bisection representations for the performance and cognitive states of students, thus limiting the application scope of these models and the accuracy of the evaluation of student cognitive performance in learning processes. Therefore, fuzzy Bayesian KT models (namely, FBKT and T2FBKT) are proposed to address continuous score scenarios (e.g., subjective examinations) so that the applicability of KT models may be broadened. Moreover, fine-grained cognitive states can be discerned. In particular, referring to type-2 fuzzy theory, T2FBKT mitigates the model uncertainty of FBKT induced by uncertain parameters. Finally, extensive experiments demonstrate the effectiveness of the proposed fuzzy KT models.
Keywords:
Hidden Markov models
Bayes methods
Uncertainty
Cognition
Deep learning
Data models
Predictive models
Educational data mining
fuzzy theory
hidden Markov model (HMM)
knowledge tracing (KT)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.9
Papers:
4.9K
Citations:
2.9W