Deep Learning for MOOCs Course Recommendation Systems: State of the Art Survey

Alahmadi, Dimah; Alruwaili, Fatimah

:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8

FEATURE PEER-REVIEWED ARTICLE

Vol.12(11) (2021)

Deep Learning for MOOCs Course Recommendation Systems: State of the Art Survey

Dimah Alahmadi, Fatimah Alruwaili (Information Systems Department, King Abdulaziz University, Jeddah, SAUDI ARABIA).

Disciplinary: Information Technology, Education.
➤ FullText

DOI: 10.14456/ITJEMAST.2021.227

Keywords: Recommendation system; MOOC; Deep Learning method; Literature Review; Deep course recommendation system; Personalized learning.

Abstract
The integration of the resources of massive open online courses (MOOCs) for the learning process is crucial. The power of the Internet and big data analysis technology brings the ultimate benefits for learners. With the help of the recommendation systems (RSs), the complexity in finding the needed learning materials is limited. MOOCs-based RSs provide suggested quality of courses to learners. Recently Deep Learning techniques have evolved to enhance MOOC's course recommendation results. This survey investigated different deep learning techniques in MOOCs for course recommendation due to the high performance and significant performance of these special types of neural network algorithms. This survey contributes to the field of MOOCs recommendation systems by overviewing the current research trends and the use of different deep learning models with MOOCs recommendation systems. Literature in the utilization of deep course recommendation systems is promising and outperforms the traditional recommendation techniques.
Paper ID: 12A11Q

Cite this article:

Alahmadi, D., Alruwaili, F. (2021). Deep Learning for MOOCs Course Recommendation Systems: State of the Art Survey. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(11), 12A11Q, 1-9. http://doi.org/10.14456/ITJEMAST.2021.227

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