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Recommendation for Higher Education Candidates: A Case Study on Engineering Programs

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

Over the years, there have been created new applications recurring to automatic discovery of information in educational data. The recommendation of undergraduate programs to high school students is one of these applications with little researching so far. This can be explained by the existence of a small data quantity in this context, and traditional recommendation systems demand a large number of items and users.

In this paper, we propose a hybrid approach, combining a collaborative filtering and content-based architecture, focused on programs and students. Our system suggest programs to the candidates that guarantee a high successful academic path by predicting their grades.

Supported by national funds by Fundação para a Ciência e Tecnologia (FCT) through project GameCourse (PTDC/CCI-CIF/30754/2017).

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Notes

  1. 1.

    http://surpriselib.com/.

References

  1. Al-Badarneh, A., Alsakran, J.: An automated recommender system for course selection. Int. J. Adv. Comput. Sci. Appl. 7, 166–175 (2016). https://doi.org/10.14569/IJACSA.2016.070323

    Article  Google Scholar 

  2. Carballo, F.O.G.: Masters courses recommendation: Exploring collaborative filtering and singular value decomposition with student profiling (2014)

    Google Scholar 

  3. Jariha, P., Jain, S.K.: A state-of-the-art recommender systems: an overview on concepts, methodology and challenges. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1769–1774 (2018)

    Google Scholar 

  4. Ma, B., Taniguchi, Y., Konomi, S.: Course recommendation for university environment. In: EDM (2020)

    Google Scholar 

  5. Morsomme, R., Alferez, S.V.: Content-based course recommender system for liberal arts education. In: EDM (2019)

    Google Scholar 

  6. O’Mahony, M.P., Smyth, B.: A recommender system for on-line course enrolment: an initial study. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 133–136. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1297231.1297254

  7. Polyzou, A., Nikolakopoulos, A.N., Karypis, G.: Scholars walk: a markov chain framework for course recommendation (May 2019)

    Google Scholar 

  8. Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, vol. 1–35, pp. 1–35 (October 2010)

    Google Scholar 

  9. de Sousa, A.I.N.A.: Market-based higher education course recommendation (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Bruno Mota da Silva & Cláudia Antunes

Authors
  1. Bruno Mota da Silva
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  2. Cláudia Antunes
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Corresponding author

Correspondence to Bruno Mota da Silva .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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da Silva, B.M., Antunes, C. (2022). Recommendation for Higher Education Candidates: A Case Study on Engineering Programs. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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