Comparative Analysis of Machine Learning Techniques for Predicting Student and Teacher Performance | IJCT Volume 12 – Issue 6 | IJCT-V12I6P21
International Journal of Computer Techniques
ISSN 2394-2231
Author
Jitendra Kumar Gupta, Abhinav Shukla, Ayush Kumar Agrawaln, Vanita Jain
Abstract
Instructive analytics have been greatly one-sided by the development of machine learning ml approaches especially in the range of student and teacher concert prediction support vector machine (SVM), random forest (RF), Decision Tree (DT) also Naive Bayes (NB) classifier popular machine learning models are compared. In this training for their ability to predict instructor and student efficiency academic records attendance and teacher assessment results make up the substantial used to train the procedures accuracy recall and f1-score are used to measure performance according to the results RF performs better than SVM in terms of accuracy and interpretability which makes it a superior choice for information analytics in education.
Keywords
Educational analytics, teacher and student performance evaluation and machine learning.
Conclusion
The training concludes that because of its interpretability and robustness rf is the better model for forecasting instructor and student performance for better predictions forthcoming investigation might study real- time adaptive knowledge representations and deep learning approaches
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How to Cite This Paper
Jitendra Kumar Gupta, Abhinav Shukla, Ayush Kumar Agrawaln, Vanita Jain (2025). Comparative Analysis of Machine Learning Techniques for predicting Student and Teacher Performance. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
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