Using Data-Driven Discovery of Better Student Models to Improve Student Learning
Item
- Title
- Using Data-Driven Discovery of Better Student Models to Improve Student Learning
- Abstract/Description
- Deep analysis of domain content yields novel insights and can be used to produce better courses. Aspects of such analysis can be performed by applying AI and statistical algorithms to student data collected from educational technology and better cognitive models can be discovered and empirically validated in terms of more accurate predictions of student learning. However, can such improved models yield improved student learning? This paper reports positively on progress in closing this loop. We demonstrate that a tutor unit, redesigned based on data-driven cognitive model improvements, helped students reach mastery more efficiently. In particular, it produced better learning on the problem-decomposition planning skills that were the focus of the cognitive model improvements.
- Date
- In publication
- Artificial Intelligence in Education
- Editor
- Lane, H. Chad
- Yacef, Kalina
- Mostow, Jack
- Pavlik, Philip
- Series
- Lecture Notes in Computer Science
- Pages
- 421-430
- Publisher
- Springer
- Resource type
- en Research/Scholarly Media
- Resource status/form
- en Published Text
- Scholarship genre
- en Empirical
- Language
- en
- Open access/full-text available
- en Yes
- Peer reviewed
- en Yes
- ISBN
- 978-3-642-39112-5
- Citation
- Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., & Nixon, T. (2013). Using Data-Driven Discovery of Better Student Models to Improve Student Learning. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 421–430). Springer. https://doi.org/10.1007/978-3-642-39112-5_43
- Place
- Berlin, Heidelberg
- Item sets
- Handbook Chapter 20 Citations
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