Group members: Fulei Zhang, Yahan Hu, Shipei Feng, Wenshan Liang, Naili Ding
Problem: In recent years, there is a rapidly growing amount of students who choose to take online courses through several online learning platforms like Coursera, Udemy, Edx etc. However, the current recommendation system of those online learning platforms are messy and not helpful enough for students to take the most suitable courses based on their knowledge basis and interests. Also, for platforms providing those courses, they haven’t taken the user data thoroughly enough to optimize their courses design as well as customized promotion strategy to individuals.
Objective:
- Based on the current situation, we decided to utilize business analytics models through user data to better help online learning platforms recommend suitable courses to individual student.
- Given user activities, we attempt to help platform better understand reasons behind user actions and further optimize their courses design(eg. Difficulties, universities, teachers) and differential marketing strategies.
Data set: There’s a variety of datasets we would utilize for this analysis, obtained from Open University Learning Analytics Datasets. They contain, but are not limited to, students’ demographic information, the registration information of the courses student registered, the assessments of each course module etc. This analysis will be involved with comprehensive data pre-processing as well as rigorous exploratory data analysis attempts. We would also expect to incorporate more relevant datasets as our analysis moves forward.