השוואת שיטות

	מעקב אחר ידע	כריה של דפוסים סדרתיים (Sequential Pattern Mining)
תחום	אנליטיקה חינוכית	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1994	1995
הוגה השיטה	Albert Corbett & John Anderson	Rakesh Agrawal & Ramakrishnan Srikant
סוג	Probabilistic student modeling	Unsupervised pattern discovery
מקור מכונן	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.	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.
כינויים	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği
קשורות	3	3
תקציר	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.	Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis.