Skip to main content
SpringerLink
Log in

Tutorial: Educational Recommender Systems

  • Conference paper
  • First Online:
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1831))

Included in the following conference series:

  • 2742 Accesses

Abstract

Recommender systems (RecSys) have found widespread use in a variety of applications, including e-commerce platforms like Amazon.com and eBay, online streaming services such as YouTube, Netflix, and Spotify, and social media sites like Facebook and Twitter. The success of these applications in improving user experience and decision making by providing personalized recommendations highlights the effectiveness of RecSys. Over the past few decades, RecSys has also made its way into the field of education, which results in the development of educational recommender systems (EdRec). Its applications in this field include personalized learning experiences, recommending appropriate formal or informal learning materials, suggesting learning peers, and adapting learning to context-aware or mobile environments, and so forth. Recently, the development of RecSys has been advanced by a series of interesting and promising topics, such as multi-task learning, multi-objective optimization, multi-stakeholder considerations, concerns of fairness, accountability, and transparency, etc. However, this progress made in the field of recommender systems was not adequately disseminated to the education community or the development of EdRec. In this tutorial, we will introduce the background, motivations, knowledge & skills associated with the current development of EdRec, and discuss a set of emerging topics and open challenges, along with case studies in EdRec.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdollahpouri, H., et al.: Multistakeholder recommendation: survey and research directions. User Model. User-Adap. Inter. 30(1), 127–158 (2020). https://doi.org/10.1007/s11257-019-09256-1

    Article  Google Scholar 

  2. Adomavicius, G., Manouselis, N., Kwon, Y.O.: Multi-criteria recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 769–803. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_24

    Chapter  Google Scholar 

  3. Burke, R., Zheng, Y., Riley, S.: Experience discovery: hybrid recommendation of student activities using social network data. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 49–52 (2011)

    Google Scholar 

  4. Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 919–959. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_27

    Chapter  Google Scholar 

  5. Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_12

    Chapter  Google Scholar 

  6. Erdt, M., Fernandez, A., Rensing, C.: Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Trans. Learn. Technol. 8(4), 326–344 (2015)

    Article  Google Scholar 

  7. Garcia-Martinez, S., Hamou-Lhadj, A.: Educational recommender systems: a pedagogical-focused perspective. In: Tsihrintzis, G., Virvou, M., Jain, L. (eds.) Multimedia Services in Intelligent Environments: Recommendation Services, pp. 113–124. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00375-7_8

  8. Gómez, E., Shui Zhang, C., Boratto, L., Salamó, M., Marras, M.: The winner takes it all: geographic imbalance and provider (un) fairness in educational recommender systems. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1808–1812 (2021)

    Google Scholar 

  9. Guruge, D.B., Kadel, R., Halder, S.J.: The state of the art in methodologies of course recommender systems-a review of recent research. Data 6(2), 18 (2021)

    Article  Google Scholar 

  10. Khribi, M.K., Jemni, M., Nasraoui, O.: Recommendation systems for personalized technology-enhanced learning. In: Kinshuk, Huang, R. (eds.) Ubiquitous Learning Environments and Technologies, pp. 159–180. Springer, Heidelberg (2015)

    Google Scholar 

  11. Kirkwood, A., Price, L.: Technology-enhanced learning and teaching in higher education: what is ‘enhanced’ and how do we know? A critical literature review. Learn. Media Technol. 39(1), 6–36 (2014)

    Article  Google Scholar 

  12. Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-z

    Article  Google Scholar 

  13. Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 677–702. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_21

    Chapter  Google Scholar 

  14. Nabizadeh, A.H., Leal, J.P., Rafsanjani, H.N., Shah, R.R.: Learning path personalization and recommendation methods: a survey of the state-of-the-art. Expert Syst. Appl. 159, 113596 (2020)

    Article  Google Scholar 

  15. Pesovski, I., Bogdanova, A.M., Trajkovik, V.: Systematic review of the published explainable educational recommendation systems. In: 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET), pp. 1–8. IEEE (2022)

    Google Scholar 

  16. Salazar, C., Aguilar, J., Monsalve-Pulido, J., Montoya, E.: Affective recommender systems in the educational field. a systematic literature review. Comput. Sci. Rev. 40, 100377 (2021)

    Google Scholar 

  17. Thongchotchat, V., Kudo, Y., Okada, Y., Sato, K.: Educational recommendation system utilizing learning styles: a systematic literature review. IEEE Access (2023)

    Google Scholar 

  18. Uddin, I., Imran, A.S., Muhammad, K., Fayyaz, N., Sajjad, M.: A systematic mapping review on MOOC recommender systems. IEEE Access 9, 118379–118405 (2021)

    Article  Google Scholar 

  19. Vassileva, J.: Toward social learning environments. IEEE Trans. Learn. Technol. 1(4), 199–214 (2008)

    Article  Google Scholar 

  20. Verbert, K., et al.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)

    Article  Google Scholar 

  21. Wang, F., Zhang, L., Xu, X.: A literature review and classification of book recommendation research. J. Inf. Syst. Technol. Manag. 5(16), 15–34 (2020)

    Google Scholar 

  22. Wang, Y., Ma, W., Zhang, M., Liu, Y., Ma, S.: A survey on the fairness of recommender systems. ACM Trans. Inf. Syst. 41(3), 1–43 (2023)

    Article  Google Scholar 

  23. Wang, Y., et al.: Multi-task deep recommender systems: a survey. arXiv preprint arXiv:2302.03525 (2023)

  24. Zheng, Y.: Exploring user roles in group recommendations: a learning approach. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 49–52 (2018)

    Google Scholar 

  25. Zheng, Y.: Identifying dominators and followers in group decision making based on the personality traits. In: The HUMANIZE Workshop at ACM Conference on Intelligent User Interfaces (2018)

    Google Scholar 

  26. Zheng, Y.: ITM-Rec: an open data set for educational recommender systems. In: Companion Proceedings of the 13th International Conference on Learning Analytics & Knowledge (LAK) (2023)

    Google Scholar 

  27. Zheng, Y., Ghane, N., Sabouri, M.: Personalized educational learning with multi-stakeholder optimizations. In: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, pp. 283–289 (2019)

    Google Scholar 

  28. Zheng, Y., Mobasher, B.: Context-Aware Recommendations, pp. 173–202. World Scientific Publishing (2018)

    Google Scholar 

  29. Zheng, Y., Subramaniyan, A.: Personality-aware recommendations: an empirical study in education. Int. J. Grid Util. Comput. 12(5–6), 524–533 (2021)

    Article  Google Scholar 

  30. Zheng, Y., Toribio, J.R.: The role of transparency in multi-stakeholder educational recommendations. User Model. User-Adap. Inter. 31(3), 513–540 (2021). https://doi.org/10.1007/s11257-021-09291-x

    Article  Google Scholar 

  31. Zheng, Y., Wang, D.: Multi-criteria ranking: next generation of multi-criteria recommendation framework. IEEE Access 10, 90715–90725 (2022)

    Article  Google Scholar 

  32. Zheng, Y., Wang, D.X.: A survey of recommender systems with multi-objective optimization. Neurocomputing 474, 141–153 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology and Management, Illinois Institute of Technology, Chicago, IL, 60616, USA

    Yong Zheng

Authors
  1. Yong Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Zheng .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. University of Leeds, Leeds, UK

    Vania Dimitrova

  4. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  5. UNED, Madrid, Spain

    Olga C. Santos

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, Y. (2023). Tutorial: Educational Recommender Systems. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36336-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36335-1

  • Online ISBN: 978-3-031-36336-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation