Detecting Learning Moment-by-Moment
ARTICLE
Ryan S. J. D. Baker, Adam B. Goldstein, Neil T. Heffernan
IJAIE Volume 21, Number 1, ISSN 1560-4292
Abstract
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a specific problem step (instead of at the next or previous problem step). We use this model to analyze which KCs are learned gradually, and which are learned in "eureka" moments. We also discuss potential ways that this model could be used to improve the effectiveness of cognitive mastery learning. (Contains 5 figures and 2 tables.)
Citation
Baker, R.S.J.D., Goldstein, A.B. & Heffernan, N.T. (2011). Detecting Learning Moment-by-Moment. International Journal of Artificial Intelligence in Education, 21(1), 5-25. Retrieved March 28, 2024 from https://www.learntechlib.org/p/69589/.
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Keywords
- Computer Uses in Education
- educational technology
- Equations (Mathematics)
- Feedback (Response)
- High School Students
- Homework
- intelligent tutoring systems
- Knowledge Level
- Mastery Learning
- Mathematics Instruction
- Mathematics Skills
- Middle School Students
- models
- Predictor Variables
- Probability
- problem solving
- Skill Development
- Standardized Tests