Privacy-preserving Learner Modeling

Privacy-Preserving Learner Modeling is a learner modeling approach that belongs to the broader category of educational data mining techniques, distinguished by its integration of computational methods to protect individual student data隐私 while enabling effective inference of learning-related patterns and personalization.

18

Publications

1.6K

Citations

70

Authors

33

Institutions

Publications per year

2017–2026

13

Authors

70

  1. YM
    Yossi Matias

    Google (United States), United States

    2 publications · 51 citations

    2

    h-index

  2. SE
    Sofia Erell

    Google (Israel), Israel

    2 publications · 51 citations

    2

    h-index

  3. HN
    Hootan Nakhost

    University of Alberta, Canada

    2 publications · 51 citations

    2

    h-index

  4. AF
    Amir Feder

    Technion – Israel Institute of Technology, Israel

    2 publications · 51 citations

    2

    h-index

  5. IL
    Itay Laish

    Google (Israel), Israel

    2 publications · 51 citations

    2

    h-index

1–5 of 70

Venues

  1. IEEE Access

    2 publications · 85 citations

    2

    h-index

  2. Educational Technology Research and Development

    ETRD · 1 publications · 271 citations

    1

    h-index

  3. Magna Scientia Advanced Research and Reviews

    1 publications · 169 citations

    1

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  4. Programming and Computer Software

    1 publications · 18 citations

    1

    h-index

  5. Theory and Research in Education

    TRE · 1 publications · 35 citations

    1

    h-index

1–5