Week 1 — MOOC Recommendation
Hi everyone. Welcome to the first blog about our MOOC Recommendation System project. This semester’s AIN311: Fundamentals of Machine Learning course project theme is education. So, we decided to make a MOOC recommendation system. We will share our progress via this blog and this week, are going to describe what we are planning to do. Without further ado, let’s get into the details.
— What is MOOC? —
The definition at mooc.org: “Massive Open Online Courses (MOOCs) are free online courses available for anyone to enroll. MOOCs provide an affordable and flexible way to learn new skills, advance your career and deliver quality educational experiences at scale.” With the pandemic, these courses became much more popular. The convenience of learning something at any time and any place is what tempts people. Some examples of MOOC sites are Udemy, edX, and Coursera. Considering the convenience and practicality these courses provide to people and students, we decided to work on this topic.
— Problem Definition —
Online learning platforms are becoming more popular year after year. Udemy is one of these platforms. Udemy has tons of courses with specialized categories such as personal development, business, marketing, etc. And it is important to find appropriate courses for students. In this way, students will not waste their time finding related courses for their objectives.
For this purpose, we will make a recommendation system using collaborative filtering and other ML methods. And for data, we will use data available on the internet and will generate data if needed.
That’s it for today. Thank you for reading. Hope to see you next week.
- Arif Enes Aydın (@Arif Enes Aydın)
- Muhammet Ali Şentürk
— Related Works —
Jing Li, Zhou Ye, “Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm”, Complexity, vol. 2020, Article ID 6619249, 10 pages, 2020. https://doi.org/10.1155/2020/6619249
Khanal, S.S., Prasad, P., Alsadoon, A. et al. A systematic review: machine learning based recommendation systems for e-learning. Educ Inf Technol 25, 2635–2664 (2020). https://doi.org/10.1007/s10639-019-10063-9
Pirasteh, P., Bouguelia, MR. & Santosh, K.C. Personalized recommendation: an enhanced hybrid collaborative filtering. Adv. in Comp. Int. 1, 1 (2021). https://doi.org/10.1007/s43674-021-00001-z
