Student Performance and Engagement Prediction in eLearning datasets

This repository contains 4 folders:

Student Performance Prediction - Binary Scenario: Contains the dataset and description used as part of our work to predict student performance using binary ML models.
Student Performance Prediction - Multiclass Case: Contains the dataset and description used as part of our work to predict student performance using multiclass ML models.
Student Engagement Level Prediction - Binary Case: Contains the dataset and description used as part of our work to predict student engagement using binary ML models.
Student Engagement Level Prediction - Multiclass Case: Contains the dataset and description used as part of our work to predict student engagement using multiclass ML models.

The data can be accessed and downloaded at the following link: https://github.com/Western-OC2-Lab/Student-Performance-and-Engagement-Pr...

Please cite the following works when using these datasets:
- M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Systematic ensemble model selection approach for educational data mining,” Knowledge-based Systems, vol. 200, p. 105992, Jul. 2020.
- M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Multi-split optimized bagging ensemble model selection for multiclass educational data mining,” Applied Intelligence, 50, pp. 4506–4528, Jul. 2020.
- A. Moubayed, M. Injadat, A. B. Nassif, H. Lutfiyya and A. Shami, "E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics," in IEEE Access, vol. 6, pp. 39117-39138, 2018.
- A. Moubayed, M. Injadat, A. Shami, and H. Lutfiyya, “Student Engagement Level in an e-Learning Environment: Clustering Using K-means”, American Journal of Distance Education, 34:2, pp. 137-156, Mar. 2020
- A. Moubayed, M. Injadat, A. Shami, and H. Lutfiyya, "Relationship Between Student Engagement and Performance in E-Learning Environment Using Association Rules," 2018 IEEE World Engineering Education Conference (EDUNINE), Buenos Aires, 2018, pp. 1-6.

Contact Information:

Feel free to contact us for any questions or collaboration opportunities.

Dr. MohammadNoor Injadat: minjadat@uwo.ca

Dr. Abdallah Moubayed: amoubaye@uwo.ca