Recommender Systems For Learning

Recommender Systems for Learning is a computational framework within personalized learning systems that specifically generates individualized educational recommendations by analyzing learners' behaviors, preferences, and knowledge states to optimize learning pathways and resource selection.

784

Publications

32.1K

Citations

2K

Authors

749

Institutions

Publications per year

2017–2026

421

Authors

2K

  1. HD
    Hendrik Drachsler

    Open University of the Netherlands, Netherlands

    14 publications · 1.4K citations

    14

    h-index

  2. NM
    Nikos Manouselis

    Agricultural University of Athens, Greece

    10 publications · 1K citations

    10

    h-index

  3. HH
    Hans Hummel

    Open University of the Netherlands, Netherlands

    8 publications · 578 citations

    8

    h-index

  4. ZN
    Zhendong Niu

    Beijing Institute of Technology, China

    8 publications · 1.1K citations

    8

    h-index

  5. AK
    Aleksandra Klašnja‐Milićević

    University of Novi Sad, Serbia

    8 publications · 1.1K citations

    8

    h-index

1–5 of 2K

Venues

  1. Procedia Computer Science

    21 publications · 1.2K citations

    18

    h-index

  2. IEEE Transactions on Learning Technologies

    19 publications · 1.3K citations

    16

    h-index

  3. 16

    h-index

  4. Expert Systems with Applications

    15 publications · 814 citations

    15

    h-index

  5. Education and Information Technologies

    14 publications · 731 citations

    14

    h-index

1–5