UPSI Digital Repository (UDRep)
Start | FAQ | About
Menu Icon

QR Code Link :

Type :Final Year Project
Subject :LB Theory and practice of education
Main Author :Ashril Idhzuan Abidin
Title :Development of commercial web portal: course recommendation system module (CRSM) for xcelearn E-Learning platform
Hits :178
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2023
Corporate Name :Perpustakaan Tuanku Bainun
PDF Guest :Click to view PDF file
PDF Full Text :You have no permission to view this item.

Abstract : Perpustakaan Tuanku Bainun
Due to the COVID-19, E-Learning platform started to grow rapidly and many companies have already developed many web and mobile applications that can support real-time education. One of the issues is that people start to lose interest in this kind of education because they do not have any clue what ideal courses for them to learn. Detecting ideal courses for students to learn is a bit boring and messy work for them since they do not have any idea of where to start. So, in order to mitigate this problem, a recommendation system (RS) was proposed. The focus of this research is on developing a recommendation system in Xcelearn commercial web portal for detecting ideal courses for students to learn by answering survey questions

References

Patel, K., & Patel, H. B. (2020). A state-of-the-art survey on recommendation systems

and prospective extensions. Computers and Electronics in Agriculture, 178,

105779. doi:10.1016/j.compag.2020.105779

 

Lin, J., Pu, H., Li, Y., & Lian, J. (2018). Intelligent Recommendation System for

Course Selection in Smart Education. Procedia Computer Science, 129, 449–453.

doi:10.1016/j.procs.2018.03.023

 

Ibrahim, T. S., Saleh, A. I., Elgaml, N., & Abdelsalam, M. M. (2020). A fog based

recommendation system for promoting the performance of E-Learning

environments. Computers & Electrical Engineering, 87, 106791.

doi:10.1016/j.compeleceng.2020.106791

 

Campos, R., Pereira dos Santos, R., & Oliveira, J. (2018). Web-Based

Recommendation System Architecture for Knowledge Reuse in MOOCs

Ecosystems. 2018 IEEE International Conference on Information Reuse and

Integration (IRI). doi:10.1109/iri.2018.00036

 

Cui, Z., Xu, X., Xue, F., Cai, X., Cao, Y., Zhang, W., & Chen, J. (2020). Personalized

Recommendation System based on Collaborative Filtering for IoT Scenarios.

IEEE Transactions on Services Computing, 1–1. doi:10.1109/tsc.2020.2964552

 

Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N.,

Malathi, D., & Senthilselvan, N. (2019). An ontology-driven personalized food

recommendation in an IoT-based healthcare system. The Journal of

Supercomputing, 75(6), 3184-3216.

 

Lin, J., Pu, H., Li, Y., & Lian, J. (2018). Intelligent recommendation system for course

selection in smart education. Procedia Computer Science, 129, 449-453.

 

Toskova, A., & Penchev, G. (2021, March). Intelligent game recommendation system.

In AIP Conference Proceedings (Vol. 2333, No. 1, p. 050007). AIP Publishing

LLC.

 

Jung, Y., Braiman, J., McNamara, M., & Hunse, W. (2021). Virtual Assistant for

Regulation-Dense Organizations. 2021 The 4th International Conference on

Information Science and Systems. doi:10.1145/3459955.3460609

 

Bagwan, K. I., & Ghule, S. D. (2021). A Modern Review on Laravel- PHP Framework.

International Journal of Advanced Research in Computer Science, 12(2), 59-66.


This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to search page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries, kindly contact us at pustakasys@upsi.edu.my or 016-3630263. Office hours only.