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Recommendation of Pedagogical Resources Based on Learners’ Profiles

Year 2023, Volume: 6 Issue: 1, 1 - 10, 16.06.2023
https://doi.org/10.53508/ijiam.1213949

Abstract

To help the learner mind in his selection process, several recommender systems have attracted researchers in e-learning systems. The recommendation system solves the problem of overload information due to the multitude of resources and interactions. How select the appropriate pedagogical resources and providing suitable ones for learners is the main objective of this work. Thus, in order to help these learners, we take into account their social relationships, the course they follow and their evaluation according to their levels. These factors are extracted to define three types of recommendations: the recommendation of the most visited resources, the recommendation of the most evaluated resources and the recommendation of the useful resources. All these propositions are used by an approach that is solid, new and solves the cold start problem. It is adopted by a system called RR-LEARNER (Resources Recommendation for LEARNER) where its use gives good results.

References

  • Ravinder, K. (2017). The effect of collaborative learning on enhancing student achievement: A meta-analysis (Doctoral dissertation, Concordia University), Montreal, Quebec, Canada.
  • Sharma, R. S., Shaikh, A. A., & Li, E. (2021). Designing Recommendation or Suggestion Systems: looking to the future. Electronic Markets, 31(2), 243-252.
  • Erdt, M., Fernandez, A., & Rensing, C. (2015). Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Transactions on Learning Technologies, 8(4), 326-344.
  • George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
  • Mehenaoui, Z. (2018). Recommandation de collaborateurs pertinents dans un environnement d'apprentissage collaboratif. Thèse de doctorat en sciences. Université Badji Mokhtar-Annaba, Algérie.
  • Madani, Y., Erritali, M., Bengourram, J., & Sailhan, F. (2019). Social collaborative filtering approach for recommending courses in an E-learning platform. Procedia Computer Science, 151, 1164-1169.
  • Jing, X., & Tang, J. (2017, August). Guess you like: course recommendation in MOOCs. In Proceedings of the International Conference on Web Intelligence (pp. 783-789).
  • Mehenaoui, Z., La , Y., Seridi, H., Merzoug, M. and Abassi, A (2014). Recommandation des apprenants pertinents dans un environnement d'apprentissage collaboratif, Paper presented at 9me Conférence sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE2014), Bziers, France, 1820 November 2014.
  • Bendjebar, S., La , Y., Bencheker, Z., Drissi, M. (2017). Study of the impact of collaboration among learners in a tutoring system. The 3rd International Conference on Networking and Advanced Systems (ICNAS 2017). Annaba, Algeria.
  • Mawane, J., Naji, A., & Ramdani, M. (2020, September). Recommender E-Learning platform using sentiment analysis aggregation. In Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (pp. 1-6).
  • Wan S, Niu Z. (2019).A hybrid e-learning recommendation approach based on learners influence propagation. IEEE Transactions on Knowledge and Data Engineering 2019;32:827-40.
  • Mawane, J., Naji, A., & Ramdani, M. (2020, September). Recommender E-Learning platform using sentiment analysis aggregation. In Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (pp. 1-6).
  • Souabi, S., Retbi, A., Idrissi, M. K., & Bennani, S. (2020, November). A recommendation approach based on correlation and co-occurrence within social learning network. In 2020 5th International Conference on Cloud Computing and Arti cial Intelligence: Technologies and Applications (CloudTech) (pp. 1-6). IEEE.
  • Baidada, M., Mansouri, K., & Poirier, F. (2020). Hybrid recommendation approach based on a voting system: experimentation in an educational context. In Proceeding de la conférence internationale e-Learning (pp. 31-38).
  • Ndiyae, N. M., Chaabi, Y., Lekdioui, K., & Lishou, C. (2019, March). Recommending system for digital educational resources based on learning analysis. In Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classifi cation Society (pp. 1-6).
Year 2023, Volume: 6 Issue: 1, 1 - 10, 16.06.2023
https://doi.org/10.53508/ijiam.1213949

Abstract

References

  • Ravinder, K. (2017). The effect of collaborative learning on enhancing student achievement: A meta-analysis (Doctoral dissertation, Concordia University), Montreal, Quebec, Canada.
  • Sharma, R. S., Shaikh, A. A., & Li, E. (2021). Designing Recommendation or Suggestion Systems: looking to the future. Electronic Markets, 31(2), 243-252.
  • Erdt, M., Fernandez, A., & Rensing, C. (2015). Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Transactions on Learning Technologies, 8(4), 326-344.
  • George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
  • Mehenaoui, Z. (2018). Recommandation de collaborateurs pertinents dans un environnement d'apprentissage collaboratif. Thèse de doctorat en sciences. Université Badji Mokhtar-Annaba, Algérie.
  • Madani, Y., Erritali, M., Bengourram, J., & Sailhan, F. (2019). Social collaborative filtering approach for recommending courses in an E-learning platform. Procedia Computer Science, 151, 1164-1169.
  • Jing, X., & Tang, J. (2017, August). Guess you like: course recommendation in MOOCs. In Proceedings of the International Conference on Web Intelligence (pp. 783-789).
  • Mehenaoui, Z., La , Y., Seridi, H., Merzoug, M. and Abassi, A (2014). Recommandation des apprenants pertinents dans un environnement d'apprentissage collaboratif, Paper presented at 9me Conférence sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE2014), Bziers, France, 1820 November 2014.
  • Bendjebar, S., La , Y., Bencheker, Z., Drissi, M. (2017). Study of the impact of collaboration among learners in a tutoring system. The 3rd International Conference on Networking and Advanced Systems (ICNAS 2017). Annaba, Algeria.
  • Mawane, J., Naji, A., & Ramdani, M. (2020, September). Recommender E-Learning platform using sentiment analysis aggregation. In Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (pp. 1-6).
  • Wan S, Niu Z. (2019).A hybrid e-learning recommendation approach based on learners influence propagation. IEEE Transactions on Knowledge and Data Engineering 2019;32:827-40.
  • Mawane, J., Naji, A., & Ramdani, M. (2020, September). Recommender E-Learning platform using sentiment analysis aggregation. In Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (pp. 1-6).
  • Souabi, S., Retbi, A., Idrissi, M. K., & Bennani, S. (2020, November). A recommendation approach based on correlation and co-occurrence within social learning network. In 2020 5th International Conference on Cloud Computing and Arti cial Intelligence: Technologies and Applications (CloudTech) (pp. 1-6). IEEE.
  • Baidada, M., Mansouri, K., & Poirier, F. (2020). Hybrid recommendation approach based on a voting system: experimentation in an educational context. In Proceeding de la conférence internationale e-Learning (pp. 31-38).
  • Ndiyae, N. M., Chaabi, Y., Lekdioui, K., & Lishou, C. (2019, March). Recommending system for digital educational resources based on learning analysis. In Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classifi cation Society (pp. 1-6).
There are 15 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Safia Bendjebar

Nour El Islem Djebarnia

Zohra Mehenaoui

Yacine Lafifi

Early Pub Date June 3, 2023
Publication Date June 16, 2023
Acceptance Date February 18, 2023
Published in Issue Year 2023 Volume: 6 Issue: 1

Cite

APA Bendjebar, S., Djebarnia, N. E. I., Mehenaoui, Z., Lafifi, Y. (2023). Recommendation of Pedagogical Resources Based on Learners’ Profiles. International Journal of Informatics and Applied Mathematics, 6(1), 1-10. https://doi.org/10.53508/ijiam.1213949
AMA Bendjebar S, Djebarnia NEI, Mehenaoui Z, Lafifi Y. Recommendation of Pedagogical Resources Based on Learners’ Profiles. IJIAM. June 2023;6(1):1-10. doi:10.53508/ijiam.1213949
Chicago Bendjebar, Safia, Nour El Islem Djebarnia, Zohra Mehenaoui, and Yacine Lafifi. “Recommendation of Pedagogical Resources Based on Learners’ Profiles”. International Journal of Informatics and Applied Mathematics 6, no. 1 (June 2023): 1-10. https://doi.org/10.53508/ijiam.1213949.
EndNote Bendjebar S, Djebarnia NEI, Mehenaoui Z, Lafifi Y (June 1, 2023) Recommendation of Pedagogical Resources Based on Learners’ Profiles. International Journal of Informatics and Applied Mathematics 6 1 1–10.
IEEE S. Bendjebar, N. E. I. Djebarnia, Z. Mehenaoui, and Y. Lafifi, “Recommendation of Pedagogical Resources Based on Learners’ Profiles”, IJIAM, vol. 6, no. 1, pp. 1–10, 2023, doi: 10.53508/ijiam.1213949.
ISNAD Bendjebar, Safia et al. “Recommendation of Pedagogical Resources Based on Learners’ Profiles”. International Journal of Informatics and Applied Mathematics 6/1 (June 2023), 1-10. https://doi.org/10.53508/ijiam.1213949.
JAMA Bendjebar S, Djebarnia NEI, Mehenaoui Z, Lafifi Y. Recommendation of Pedagogical Resources Based on Learners’ Profiles. IJIAM. 2023;6:1–10.
MLA Bendjebar, Safia et al. “Recommendation of Pedagogical Resources Based on Learners’ Profiles”. International Journal of Informatics and Applied Mathematics, vol. 6, no. 1, 2023, pp. 1-10, doi:10.53508/ijiam.1213949.
Vancouver Bendjebar S, Djebarnia NEI, Mehenaoui Z, Lafifi Y. Recommendation of Pedagogical Resources Based on Learners’ Profiles. IJIAM. 2023;6(1):1-10.

International Journal of Informatics and Applied Mathematics