research-article

Recommender System to Support MOOCs Teachers: Framework based on Ontology and Linked Data

Authors:

Nour-eddine el Faddouli,

Samir BennaniAuthors Info & Claims

SITA'20: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications

Article No.: 18, Pages 1 - 7

https://doi.org/10.1145/3419604.3419619

Published: 08 November 2020 Publication History

Abstract

The proliferation of Massive Open Online Courses (MOOCs) has generated conflicting opinions about their quality. In this paper, we aim at improving the quality of MOOCs through assisting teachers and designers from the initiation phase of MOOCs. For this purpose, we propose a recommendation system Framework based on the knowledge about teachers and MOOCs. Our approach aims to overcome the problems of traditional recommendation systems, by using and integrating different techniques: modeling via ontologies, semantic web technologies, extracting and integrating Linked Data from different sources, ontology mapping and semantic similarity measures.

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Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Sebbaq HEl Faddouli N(2023)MTBERT-Attention: An Explainable BERT Model based on Multi-Task Learning for Cognitive Text ClassificationScientific African10.1016/j.sciaf.2023.e0179921(e01799)Online publication date: Sep-2023
https://doi.org/10.1016/j.sciaf.2023.e01799
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
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Index Terms

Recommender System to Support MOOCs Teachers: Framework based on Ontology and Linked Data
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)

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SITA'20: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications

September 2020

333 pages

ISBN:9781450377331

DOI:10.1145/3419604

Copyright © 2020 ACM.

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Published: 08 November 2020

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SITA'20: Theories and Applications

September 23 - 24, 2020

Rabat, Morocco

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Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Sebbaq HEl Faddouli N(2023)MTBERT-Attention: An Explainable BERT Model based on Multi-Task Learning for Cognitive Text ClassificationScientific African10.1016/j.sciaf.2023.e0179921(e01799)Online publication date: Sep-2023
https://doi.org/10.1016/j.sciaf.2023.e01799
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
Santos RAguilar J(2022)A hybrid recommender system based on description/dialetheic logic and linked dataExpert Systems10.1111/exsy.1314340:2Online publication date: 19-Sep-2022
https://doi.org/10.1111/exsy.13143
Sebbaq HEl Faddouli N(2022)An explainable attention-based bidirectional GRU model for pedagogical classification of MOOCsInteractive Technology and Smart Education10.1108/ITSE-10-2021-018819:4(396-421)Online publication date: 6-Sep-2022
https://doi.org/10.1108/ITSE-10-2021-0188
Sebbaq HEl Faddouli N(2022)Pedagogical Classification Model Based on Machine LearningEmerging Trends in Intelligent Systems & Network Security10.1007/978-3-031-15191-0_35(363-371)Online publication date: 1-Sep-2022
https://doi.org/10.1007/978-3-031-15191-0_35
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
Mahdi AHadi A(2021)The Current State of Linked Data-based Recommender Systems2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)10.1109/IT-ELA52201.2021.9773738(154-160)Online publication date: 28-Dec-2021
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Uddin IImran AMuhammad KFayyaz NSajjad M(2021)A Systematic Mapping Review on MOOC Recommender SystemsIEEE Access10.1109/ACCESS.2021.31010399(118379-118405)Online publication date: 2021
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Sebbaq HEl Faddouli N(2021)MOOCs Semantic Interoperability: Towards Unified and Pedagogically Enriched Model for Building a Linked Data RepositoryDigital Technologies and Applications10.1007/978-3-030-73882-2_56(621-631)Online publication date: 26-Jun-2021
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