Distractor Generation

Distractor Generation is a methodological construct in educational assessment that systematically produces plausible incorrect responses for multiple-choice items by leveraging cognitive or linguistic models of misconception and error.

96

Publications

3.6K

Citations

292

Authors

138

Institutions

Publications per year

2017–2026

56

Authors

292

  1. LA
    Le An Ha

    University of Wolverhampton, United Kingdom

    4 publications · 568 citations

    4

    h-index

  2. MM
    Mukta Majumder

    Haldia Institute of Technology, India

    3 publications · 104 citations

    3

    h-index

  3. JL
    John Lee

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 120 citations

    3

    h-index

  4. RM
    Ruslan Mitkov

    University of Wolverhampton, United Kingdom

    3 publications · 542 citations

    3

    h-index

  5. SK
    Sujan Kumar Saha

    Birla Institute of Technology, Mesra, India

    3 publications · 103 citations

    3

    h-index

1–5 of 292

Venues

  1. IEEE Transactions on Learning Technologies

    4 publications · 124 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 133 citations

    3

    h-index

  3. 3

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 41 citations

    3

    h-index

  5. Natural Language Engineering

    2 publications · 244 citations

    2

    h-index

1–5