เปรียบเทียบวิธี

	การวิเคราะห์การเรียนรู้	ทฤษฎีปริภูมิความรู้	Sequential Pattern Mining
สาขาวิชา	การวิเคราะห์การศึกษา	การวิเคราะห์การศึกษา	การเรียนรู้ของเครื่อง
ตระกูล	Process / pipeline	Machine learning	Machine learning
ปีกำเนิด	2011	1985	1995
ผู้ริเริ่ม	George Siemens & Phil Long	Jean-Paul Doignon & Jean-Claude Falmagne	Rakesh Agrawal & Ramakrishnan Srikant
ประเภท	data-driven educational process pipeline	Combinatorial knowledge assessment framework	Unsupervised pattern discovery
แหล่งต้นตำรับ	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.	Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175–196.	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.
ชื่อเรียกอื่น	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	KST, Knowledge Structures, Competence-Based Knowledge Space Theory, Bilgi Uzayı Teorisi	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği
ที่เกี่ยวข้อง	3	3	3
สรุป	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 Space Theory (KST) is a combinatorial, set-theoretic framework for modeling and assessing human knowledge, introduced by Jean-Paul Doignon and Jean-Claude Falmagne in 1985. It represents a learner's competence as a subset of a problem domain, organizes all feasible competence subsets into a lattice called a knowledge space, and uses probabilistic inference to locate a learner within that space. The approach underlies adaptive testing and intelligent tutoring systems, offering a mathematically rigorous alternative to classical test theory.	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.