التفاصيل البيبلوغرافية
| العنوان: |
An Introduction to Bayesian Knowledge Tracing with pyBKT. |
| المؤلفون: |
Bulut, Okan, Shin, Jinnie, Yildirim-Erbasli, Seyma N., Gorgun, Guher, Pardos, Zachary A. |
| المصدر: |
Psych; Sep2023, Vol. 5 Issue 3, p770-786, 17p |
| مصطلحات موضوعية: |
BAYESIAN analysis, PROBABILISTIC databases, PYTHON programming language, ITEM response theory, LEARNING |
| مستخلص: |
This study aims to introduce Bayesian Knowledge Tracing (BKT), a probabilistic model used in educational data mining to estimate learners' knowledge states over time. It also provides a practical guide to estimating BKT models using the pyBKT library available in Python. The first section presents an overview of BKT by explaining its theoretical foundations and advantages in modeling individual learning processes. In the second section, we describe different variants of the standard BKT model based on item response theory (IRT). Next, we demonstrate the estimation of BKT with the pyBKT library in Python, outlining data pre-processing steps, parameter estimation, and model evaluation. Different cases of knowledge tracing tasks illustrate how BKT estimates learners' knowledge states and evaluates prediction accuracy. The results highlight the utility of BKT in capturing learners' knowledge states dynamically. We also show that the model parameters of BKT resemble the parameters from logistic IRT models. [ABSTRACT FROM AUTHOR] |
|
Copyright of Psych is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
