Data-driven Hint Generation

Data-Driven Hint Generation is a computational approach to hint generation that systematically derives instructional hints from empirical data, such as learner interaction logs or performance patterns, distinguishing itself from rule-based or theory-driven methods by relying on observed behavioral evidence to inform hint content and timing.

18

Publications

1.2K

Citations

45

Authors

20

Institutions

Publications per year

2017–2026

9

Authors

45

  1. TB
    Tiffany Barnes

    North Carolina State University, United States

    8 publications · 342 citations

    8

    h-index

  2. TP
    Thomas Price

    North Carolina State University, United States

    5 publications · 163 citations

    5

    h-index

  3. JS
    John Stamper

    Carnegie Mellon University, United States

    3 publications · 187 citations

    3

    h-index

  4. BP
    Benjamin Paaßen

    Bielefeld University, Germany

    2 publications · 55 citations

    2

    h-index

  5. MJ
    Marvin J. Croy

    University of North Carolina at Charlotte, United States

    2 publications · 142 citations

    2

    h-index

1–5 of 45

Venues

  1. 3

    h-index

  2. Educational Data Mining

    Conference · 2 publications · 79 citations

    2

    h-index

  3. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 1 publications · 31 citations

    1

    h-index

  4. Educational Technology & Society

    1 publications · 45 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 12 citations

    1

    h-index

1–5