Home / Current Issue / Paper 1704432
EduNexus: A Personalized Course Recommendation System Based on Skills and Career Interests
Subject area: Science,Engineering and Technology · Area of research: Machine Learning (Recommendation System)
Abstract
With the growth of online learning platforms, students now have access to a wide choice of courses on a wide range of topics. For students, the sheer quantity of possibilities can be daunting, and selecting the correct course can be difficult. Learners spend endless hours exploring each of the online course platforms in order to find the course that best fits their interests. Finding courses that are a suitable fit for their interests and learning goals can thus be made easier for students by employing customised course recommendations. The proposed system is intended to provide users with tailored course recommendations based on their interests, skill sets, and personal preferences. Our approach in this study uses machine learning methods to provide meaningful cross-platform course recommendations.
Keywords
Recommendation system, Machine learning, Feature extraction, Data Mining, Cosine similarity
References
[1] Y. Ren, Z. He and T. Han, "Research on Optimal Design of Online Education Course Recommendation System Based on Hybrid Recommendation Algorithm," in 2021
[2] N. N. Y. Vo, N. H. Vu, T. A. Vu, Q. T. Vu and B. D. Mach, "CRS - A Hybrid Course Recommendation System for Software Engineering Education," 2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 2022.
[3] Weijie Jiang, Zachary A. Pardos, and Qiang Wei. 2019. Goal-based Course Recommendation. In Proceedings of the 9th International Conference on Learning Analytics Knowledge (LAK19). Association for Computing Machinery, New York, NY, USA, 36–45.
[4] Gulzar, Z., A, A. Deepak, G. (2018). PCRS: Personalized Course Recommender System Based on Hybrid Approach. Procedia Computer Science, 125:518–524.
[5] L. Zhao and Z. Pan, "Research on Online Course Recommendation Model Based on Improved Collaborative Filtering Algorithm," 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 2021.
[6] P. Jiang, Y. Feng, C. Niu and Y. Dai, "Study of intelligent recommendation for online video courses," 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2021, pp. 1290-1294.
How to cite this paper
@article{1704432,
author = {Bhakti Sable, Neha Bhadale, Vaishnavi Pawar, Sagar Deshmukh, Asst. Prof. Rutuja Kulkarni},
title = {EduNexus: A Personalized Course Recommendation System Based on Skills and Career Interests},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {11},
pages = {330-333},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704432.pdf},
abstract = {With the growth of online learning platforms, students now have access to a wide choice of courses on a wide range of topics. For students, the sheer quantity of possibilities can be daunting, and selecting the correct course can be difficult. Learners spend endless hours exploring each of the online course platforms in order to find the course that best fits their interests. Finding courses that are a suitable fit for their interests and learning goals can thus be made easier for students by employing customised course recommendations. The proposed system is intended to provide users with tailored course recommendations based on their interests, skill sets, and personal preferences. Our approach in this study uses machine learning methods to provide meaningful cross-platform course recommendations.},
keywords = {Recommendation system, Machine learning, Feature extraction, Data Mining, Cosine similarity},
month = {May},
}