Learning Object Recommendation

Learning Object Recommendation is a computational method that identifies and suggests educational resources tailored to individual learners' needs, preferences, and contexts by leveraging user models and domain knowledge within the broader field of Educational Recommender Systems.

91

Publications

2.9K

Citations

227

Authors

107

Institutions

Publications per year

2017–2026

35

Authors

227

  1. EB
    Enrique Barra

    Universidad Politécnica de Madrid, Spain

    4 publications · 83 citations

    4

    h-index

  2. SM
    Shaimaa M. Nafea

    Arab Academy for Science, Technology, and Maritime Transport, Egypt

    3 publications · 119 citations

    3

    h-index

  3. TK
    Ti Kai Chiu

    National Cheng Kung University, Taiwan

    3 publications · 178 citations

    3

    h-index

  4. TI
    Tzone I. Wang

    National Cheng Kung University, Taiwan

    3 publications · 178 citations

    3

    h-index

  5. YH
    Ying He

    De Montfort University, United Kingdom

    3 publications · 119 citations

    3

    h-index

1–5 of 227

Venues

  1. 4

    h-index

  2. IEEE Transactions on Learning Technologies

    3 publications · 83 citations

    3

    h-index

  3. Education and Information Technologies

    2 publications · 104 citations

    2

    h-index

  4. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 2 publications · 42 citations

    2

    h-index

  5. 2

    h-index

1–5