Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts

Keyphrases

• Performance Prediction 100%
• Non-cognitive 100%
• Potential Identification 100%
• Dropout 100%
• Cognitive Characteristics 100%
• Electrical Engineering Students 100%
• Prediction Mechanism 50%
• Dropout Rate 33%
• Student Dropout 33%
• Machine Learning Techniques 16%
• European Countries 16%
• First Year 16%
• Decision Tree 16%
• Educators 16%
• Prediction Accuracy 16%
• Poor Performance 16%
• Student Performance 16%
• Engineering Students 16%
• Engineering Discipline 16%
• Electrical Engineering 16%
• University Administration 16%
• Student Impacts 16%

Computer Science

• Engineering Student 100%
• Machine Learning Technique 50%
• Prediction Accuracy 50%
• European Country 50%
• Engineering Discipline 50%
• Decision Tree 50%

Chemical Engineering

• Learning System 100%