Educational data mining: A survey and a data mining-based analysis of recent works

•The work extends prior educational data mining (EDM) reviews and updates its history.•A data mining (DM) profile is set to depict the DM ground that supports EDM works.•An EDM approach profile is set to provide key traits for depicting EDM approaches.•A trait-value pattern is set to depict most of...

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Vydáno v:Expert systems with applications Ročník 41; číslo 4; s. 1432 - 1462
Hlavní autor: Peña-Ayala, Alejandro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Ltd 01.03.2014
Elsevier
Témata:
Applied sciences
Computer science; control theory; systems
Data mining
Data mining profile
Data processing. List processing. Character string processing
Education
Educational data mining
Educational data mining approach pattern
Electric discharge machining
Exact sciences and technology
Expert systems
Firm modelling
General aspects
Mathematical models
Memory organisation. Data processing
Occupational training. Personnel. Work management
Operational research and scientific management
Operational research. Management science
Pattern for descriptive and predictive educational data mining approaches
Preserves
Software
Tasks
Educational data mining approach pattern
Pattern for descriptive and predictive educational data mining approaches
Educational data mining
Data mining profile
Data mining
Data analysis
Statistical analysis
Cluster
Modeling
Teaching
Business model
Classification
Electronic document management
Database
Pattern for descriptive and predictive
educational data mining approaches
Content analysis
Firm strategy
ISSN:0957-4174, 1873-6793
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Popis
Shrnutí:•The work extends prior educational data mining (EDM) reviews and updates its history.•A data mining (DM) profile is set to depict the DM ground that supports EDM works.•An EDM approach profile is set to provide key traits for depicting EDM approaches.•A trait-value pattern is set to depict most of descriptive and predictive EDM models.•Three disciplines, tasks, methods, algorithms are the most used for building EDM works. This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.08.042