ML Anthology

A Dynamic Mixture Model to Detect Student Motivation and Proficiency

Jeffrey Johns, Beverly Park Woolf
AAAI 2006 pp. 163-168
/aaai/2006/johns2006aaai-dynamic/

Abstract

Unmotivated students do not reap the full rewards of using a computer-based intelligent tutoring system. Detection of improper behavior is thus an important component of an online student model. To meet this challenge, we present a dynamic mixture model based on Item Response Theory. This model, which simultaneously estimates a student’s proficiency and changing motivation level, was tested with data of high school students using a geometry tutoring system. By accounting for student motivation, the dynamic mixture model can more accurately estimate proficiency and the probability of a correct response. The model’s generality is an added benefit, making it applicable to many intelligent tutoring systems as well as other domains.

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Cite

Text

Johns and Woolf. "A Dynamic Mixture Model to Detect Student Motivation and Proficiency." AAAI Conference on Artificial Intelligence, 2006.

Markdown

[Johns and Woolf. "A Dynamic Mixture Model to Detect Student Motivation and Proficiency." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/johns2006aaai-dynamic/)

BibTeX

@inproceedings{johns2006aaai-dynamic,
  title     = {{A Dynamic Mixture Model to Detect Student Motivation and Proficiency}},
  author    = {Johns, Jeffrey and Woolf, Beverly Park},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2006},
  pages     = {163-168},
  url       = {https://mlanthology.org/aaai/2006/johns2006aaai-dynamic/}
}