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| 학습 분석× | 지식 추적× | |
|---|---|---|
| 분야 | 교육 분석학 | 교육 분석학 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 2011 | 1994 |
| 창시자≠ | George Siemens & Phil Long | Albert Corbett & John Anderson |
| 유형≠ | data-driven educational process pipeline | Probabilistic student modeling |
| 원전≠ | Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. link ↗ | 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 | Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği | BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme |
| 관련 | 3 | 3 |
| 요약≠ | Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, with the purpose of understanding and optimizing learning and the environments in which it occurs. Formally introduced by George Siemens and Phil Long in 2011, the approach draws on data generated in digital learning environments to provide educators, institutions, and learners with evidence-based feedback for improving educational outcomes. | 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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