Educational Data Mining

Educational Data Mining is a research discipline that applies data mining, machine learning, and statistical techniques to large-scale datasets generated in educational contexts to extract knowledge about learning processes and learners for the purpose of informing evidence-based educational decision-making and improving learning outcomes.

4.2K

Publications

200.7K

Citations

10K

Authors

2.7K

Institutions

Publications per year

2017–2026

2.4K

Authors

10K

  1. DG
    Dragan Gašević

    Monash University, Australia

    59 publications · 5.3K citations

    37

    h-index

  2. CR
    Cristóbal Romero

    University of Córdoba, Spain

    40 publications · 7.7K citations

    27

    h-index

  3. RS
    Ryan S. Baker

    Columbia University, United States

    38 publications · 4.2K citations

    26

    h-index

  4. SV
    Sebastián Ventura

    University of Córdoba, Spain

    34 publications · 6.9K citations

    24

    h-index

  5. SD
    Shane Dawson

    University of South Australia, Australia

    28 publications · 4.2K citations

    22

    h-index

1–5 of 10K

Venues

  1. IEEE Access

    84 publications · 4.7K citations

    39

    h-index

  2. Education and Information Technologies

    76 publications · 4.3K citations

    38

    h-index

  3. Computers & Education

    43 publications · 7.7K citations

    34

    h-index

  4. Expert Systems with Applications

    49 publications · 4.9K citations

    34

    h-index

  5. Computers in Human Behavior

    38 publications · 5.3K citations

    33

    h-index

1–5

See also

Educational Data Mining

Core Methods

Applications

Data Sources

Evaluation

Ethical Issues