Vertaile menetelmiä

	Oppimisanalytiikka	Knowledge Tracing	Sekventiaalinen kuviotunnistus
Tieteenala	Koulutusanalytiikka	Koulutusanalytiikka	Koneoppiminen
Menetelmäperhe	Process / pipeline	Machine learning	Machine learning
Syntyvuosi	2011	1994	1995
Kehittäjä	George Siemens & Phil Long	Albert Corbett & John Anderson	Rakesh Agrawal & Ramakrishnan Srikant
Tyyppi	data-driven educational process pipeline	Probabilistic student modeling	Unsupervised pattern discovery
Alkuperäislähde	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.	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.
Rinnakkaisnimet	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği
Liittyvät	3	3	3
Tiivistelmä	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.	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.