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| 학습 곡선 (연습의 동력 법칙)× | 지식 추적× | |
|---|---|---|
| 분야 | 교육 분석학 | 교육 분석학 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1936 | 1994 |
| 창시자≠ | Theodore Wright | Albert Corbett & John Anderson |
| 유형≠ | Power-law regression model | Probabilistic student modeling |
| 원전≠ | Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128. DOI ↗ | Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. DOI ↗ |
| 별칭 | Power Law of Practice, Experience Curve, Wright's Law, Öğrenme Eğrisi | BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme |
| 관련 | 3 | 3 |
| 요약≠ | The learning curve models how performance improves predictably as cumulative experience accumulates. Formalized by Theodore Wright in 1936 using aircraft manufacturing data, it expresses the relationship between the number of practice trials (or production units) and the time or cost per unit as a power-law function. It is widely applied in educational psychology, industrial engineering, health professions training, and human factors research whenever repeated task execution is the mechanism of skill acquisition. | Knowledge Tracing (KT) is a student-modeling technique that estimates, at each moment in time, the probability that a learner has mastered a target knowledge component. Introduced by Corbett and Anderson in 1994, the classical Bayesian Knowledge Tracing (BKT) model treats skill acquisition as a two-state Hidden Markov Model driven by four interpretable parameters: prior knowledge, learning rate, slip, and guess. Deep variants (DKT, DKVMN, AKT) later replaced HMMs with recurrent and transformer architectures. |
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