Investigating Factors Contributing to Student Satisfaction in Massive Open Online Learning

Primary supervisor

Guanliang Chen

Massive Open Online Courses (MOOCs), as one of the available options, are endowed with the mission to educate the world. MOOCs refer to online courses that are designed for an unlimited number of participants. In MOOCs, the learning materials are distributed over theWeb, which can be accessed by learners with internet connections anytime and anywhere. MOOCs are becoming increasingly popular. According to Class Central, by the end of 2022, there have been over hundreds of million learners enrolled in MOOCs in various MOOC platforms including edX, Coursera, etc.

Student cohort

Double Semester

Aim/outline

This project aims to apply Natural Language Processing techniques to identify factors or aspects that significantly contributing to student satisfaction in their online learning experience (e.g., instructor, course design, and assignment design). Specific tasks include: (1) applying Natural Language Processing techniques to analyse the course reviews written by students and identify characteristic keywords that are specific to different course aspects; (2) performing sentiment analysis to characterise a student's opinion regarding a specific course aspect (e.g., to what extent a student is happy with the course design of a MOOC); and (3) conducting analysis by using hierarchical regression modeling to identify the important factors of student satisfaction.

URLs/references

  • Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724.
  • Liu, S., Peng, X., Cheng, H. N., Liu, Z., Sun, J., & Yang, C. (2019). Unfolding sentimental and behavioral tendencies of learners' concerned topics from course reviews in a MOOC. Journal of Educational Computing Research, 57(3), 670-696.
  • Kastrati, Z., Imran, A. S., & Kurti, A. (2020). Weakly supervised framework for aspect-based sentiment analysis on students’ reviews of MOOCs. IEEE Access, 8, 106799-106810.

Required knowledge

  • Strong programming skills (e.g., Python)
  • Basic knowledge in Data Science, Natural Language Processing, and Machine Learning
  • The following can be a plus: (i) good at academic writing; and (ii) strong motivation in pursing a quality academic publication.