Comparar métodos

	Teoria do Espaço do Conhecimento	Mineração de Padrões Sequenciais
Área	Análise de dados educacionais	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1985	1995
Autor original	Jean-Paul Doignon & Jean-Claude Falmagne	Rakesh Agrawal & Ramakrishnan Srikant
Tipo	Combinatorial knowledge assessment framework	Unsupervised pattern discovery
Fonte seminal	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.
Outros nomes	KST, Knowledge Structures, Competence-Based Knowledge Space Theory, Bilgi Uzayı Teorisi	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği
Relacionados	3	3
Resumo	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.