Explainable Misconception Detection

Explainable Misconception Detection is an academic method within the field of educational data mining that not only identifies learners' misconceptions but also provides transparent, interpretable, and human-understandable justifications for the detected misconceptions, thereby distinguishing it from black-box approaches to misconception detection.

27

Publications

1.1K

Citations

79

Authors

30

Institutions

Publications per year

2017–2026

6

Authors

79

  1. EM
    Eric Mays

    University of Pennsylvania, United States

    1 publications · 36 citations

    1

    h-index

  2. TL
    Tania Lombrozo

    University of California, Berkeley, United States

    1 publications · 23 citations

    1

    h-index

  3. HD
    Holger Danielsiek

    TU Dortmund University, Germany

    1 publications · 124 citations

    1

    h-index

  4. ML
    Mao-Neng Li

    National Chiayi University, Taiwan

    1 publications · 27 citations

    1

    h-index

  5. PP
    Purwanto Purwanto

    Diponegoro University, Indonesia

    1 publications · 20 citations

    1

    h-index

1–5 of 79

Venues

  1. Computer Science Education

    2 publications · 132 citations

    2

    h-index

  2. Procedia - Social and Behavioral Sciences

    2 publications · 28 citations

    2

    h-index

  3. Educational Technology Research and Development

    ETRD · 1 publications · 10 citations

    1

    h-index

  4. Cypriot Journal of Educational Sciences

    CJES · 1 publications · 20 citations

    1

    h-index

  5. TechTrends

    1 publications · 26 citations

    1

    h-index

1–5