Amanote Research
Register
Sign In
Using Data Mining Techniques to Predict Students at Risk of Poor Performance
doi 10.1109/sai.2016.7556030
Full Text
Open PDF
Abstract
Available in
full text
Date
July 1, 2016
Authors
Zahyah Alharbi
James Cornford
Liam Dolder
Beatriz De La Iglesia
Publisher
IEEE
Related search
Mining Educational Data to Predict Students’ Performance Through Procrastination Behavior
Entropy
Electronic Engineering
Information Systems
Mathematical Physics
Electrical
Astronomy
Physics
Towards Reliable Prediction of Academic Performance of Architecture Students Using Data Mining Techniques
Journal of Engineering, Design and Technology
Engineering
Implementation of Data Mining to Predict Period of Students Study Using Naive Bayes Algorithm
International Journal of Engineering and Emerging Technology
Identification of Coronary Artery Disease Risk Using Data Mining Techniques
Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi
Using Data Mining to Predict Possible Future Depression Cases
International Journal of Public Health Science (IJPHS)
Rainfall Prediction Using Data Mining Techniques
International Journal of Computer Applications
Discovering Performance Evaluation Features of Faculty Members Using Data Mining Techniques to Support Decision Making
International Journal of Computer Applications
Using Data Mining to Predict Sludge and Filamentous Microorganism Sedimentation
Polish Journal of Environmental Studies
Environmental Chemistry
Environmental Science
Intelligent Video Processing Using Data Mining Techniques
ACM SIGMultimedia Records