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Student Performance and Engagement Prediction in eLearning datasets

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Citation Author(s):
Abdallah Moubayed (University of Western Ontario)
MohammadNoor Injadat (University of Western Ontario)
Abdallah Shami (University of Western Ontario)
Ali Bou Nassif (University of Sharjah)
Hanan Lutfiyya (University of Western Ontario)
Submitted by:
Abdallah Moubayed
Last updated:
DOI:
10.21227/4xkr-0f88
Data Format:
*.csv
Links:
Github repository
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Categories:
Keywords:
Student performance prediction; student engagement level; eLearning; educational data mining
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Abstract

Description:

This repository contains the datasets used as part of the OC2 lab's work on Student Performance prediction and student engagement prediction in eLearning environments using machine learning methods.

Instructions:

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-P…

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

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