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Toward a Deep Recommender System for MOOCs Platforms

Authors:

Mounia AbikAuthors Info & Claims

ICAAI '19: Proceedings of the 3rd International Conference on Advances in Artificial Intelligence

Pages 173 - 177

https://doi.org/10.1145/3369114.3369157

Published: 21 January 2020 Publication History

Abstract

With the strong emergence of open education such as MOOCs, the opportunities and contribution of non-formal learning to the acquisition of knowledge and skills have increased. This type of learning relying mainly on the motivation of the learners remains technically invisible to environments where formal learning is delivered.

To respond to this challenge, we propose a MOOCs recommender system for formal learning platforms able to recommend the effective MOOCs to learners in the formal curriculum. This system is based on Siamese LSTM networks to measure the semantic similarity between courses description. Our model has achieved good results and is competitive with the state-of-the-art system.

References

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Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1)

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Haggard. S. (2013). The Maturing of the MOOC. 1st ed. London: Department for Business, Innovation and Skills.

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Apaza, Rel Guzman, et al. "Online Courses Recommendation based on LDA." SIMBig. 2014.

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Aher, Sunita B., and L. M. R. J. Lobo. "Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data." Knowledge-Based Systems 51 (2013): 1--14.

Digital Library

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F. Bousbahi and H. Chorfi, "MOOC-Rec: A Case Based Recommender System for MOOCs", Procedia - Social and Behavioral Sciences, vol. 195, pp. 1813--1822, 2015. Available: 10.1016/j.sbspro.2015.06.395.

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Poquet, O. 2017. Social context in MOOCs. University of South Australia.

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Alexandre Yukio Ichida, Felipe Meneguzzi, and Duncan D Ruiz. Measuring semantic similarity between sentences using a siamese neural network. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 17. IEEE, 2018.

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Elvys Linhares Pontes, Stéphane Huet, Andréa Carneiro Linhares, and JuanManuel Torres-Moreno. Predicting the semantic textual similarity with siamese cnn and lstm. arXiv preprint arXiv:1810.10641, 2018.

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Cited By

Hideki Mensch Maruyama MWillig Silveira Lda Silva Júnior ECasanova GPalazzo M. de Oliveira JMaran V(2024)Recommender systems in smart campus: a systematic mappingKnowledge and Information Systems10.1007/s10115-024-02240-1Online publication date: 26-Sep-2024
https://doi.org/10.1007/s10115-024-02240-1
Maran VMaruyama MSilveira L(2023)Aplicação de técnicas de recomendação de recursos educacionais em um campus universitárioCiência e Natura10.5902/2179460X7519545(e17)Online publication date: 11-Oct-2023
https://doi.org/10.5902/2179460X75195
Salau LHamada MPrasad RHassan MMahendran AWatanobe Y(2022)State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-LearningApplied Sciences10.3390/app12231199612:23(11996)Online publication date: 24-Nov-2022
https://doi.org/10.3390/app122311996
Show More Cited By

Index Terms

Toward a Deep Recommender System for MOOCs Platforms
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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ICAAI '19: Proceedings of the 3rd International Conference on Advances in Artificial Intelligence

October 2019

253 pages

ISBN:9781450372534

DOI:10.1145/3369114

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Northumbria University: University of Northumbria at Newcastle

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 January 2020

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ICAAI 2019

ICAAI 2019: 2019 The 3rd International Conference on Advances in Artificial Intelligence

October 26 - 28, 2019

Istanbul, Turkey

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Cited By

Hideki Mensch Maruyama MWillig Silveira Lda Silva Júnior ECasanova GPalazzo M. de Oliveira JMaran V(2024)Recommender systems in smart campus: a systematic mappingKnowledge and Information Systems10.1007/s10115-024-02240-1Online publication date: 26-Sep-2024
https://doi.org/10.1007/s10115-024-02240-1
Maran VMaruyama MSilveira L(2023)Aplicação de técnicas de recomendação de recursos educacionais em um campus universitárioCiência e Natura10.5902/2179460X7519545(e17)Online publication date: 11-Oct-2023
https://doi.org/10.5902/2179460X75195
Salau LHamada MPrasad RHassan MMahendran AWatanobe Y(2022)State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-LearningApplied Sciences10.3390/app12231199612:23(11996)Online publication date: 24-Nov-2022
https://doi.org/10.3390/app122311996
Liu TWu QChang LGu T(2022)A review of deep learning-based recommender system in e-learning environmentsArtificial Intelligence Review10.1007/s10462-022-10135-255:8(5953-5980)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10135-2
Labba CBen Atitallah RBoyer A(2022)Combining Artificial Intelligence and Edge Computing to Reshape Distance Education (Case Study: K-12 Learners)Artificial Intelligence in Education10.1007/978-3-031-11644-5_18(218-230)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11644-5_18
Urdaneta-Ponte MMendez-Zorrilla AOleagordia-Ruiz I(2021)Recommendation Systems for Education: Systematic ReviewElectronics10.3390/electronics1014161110:14(1611)Online publication date: 6-Jul-2021
https://doi.org/10.3390/electronics10141611
Wang SLi Y(2021)Learning Preference Recommendation with Heterogeneous Graph Neural Networks in MOOCProceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition10.1145/3488933.3488990(629-635)Online publication date: 24-Sep-2021
https://dl.acm.org/doi/10.1145/3488933.3488990

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