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	Knowledge Tracing	LSTM
Dziedzina	Analityka edukacyjna	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	1994	1997
Twórca	Albert Corbett & John Anderson	Hochreiter, S. & Schmidhuber, J.
Typ	Probabilistic student modeling	Recurrent neural network (gated memory cell)
Źródło pierwotne	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.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Inne nazwy	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
Pokrewne	3	5
Podsumowanie	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.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.