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Deep Learning for MOOCs Course Recommendation Systems: State of the Art Survey |
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| รหัสดีโอไอ | |
| Creator | Dimah Alahmadi, Fatimah Alruwaili |
| Title | Deep Learning for MOOCs Course Recommendation Systems: State of the Art Survey |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2564 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 12 |
| Journal No. | 11 |
| Page no. | 12A11Q: 1-9 |
| Keyword | Recommendation system, MOOC, Deep Learning method, Literature Review, Deep course recommendation system, Personalized learning. |
| URL Website | http://TuEngr.com/Vol12-11.html |
| Website title | ITJEMAST V12(11) 2021 @ TuEngr.com |
| ISSN | 2228-9860 |
| 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. |
