FORCASTING ACADMIC PERFORMANCE IN COMPUTER SCIENCE STUDENTS BASEDON FUTURE ANALYSIS METHOD
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Abstract
The ever increasing importance of education has drivenresearchers and educators to seek innovative methods forenhancing student performance and understanding the factorsthat contribute to academic success. This paper presents a methodology for predicting CGPA SGPA that leverages machine learning techniques to forecast students'academic achievements based on a variety of features, such asdemographic information, academic history, and behavioural patterns. The proposed students academic performance method utilizes a real-world collected dataset from multiple educational institutions toensure an accurate and comprehensive analysis. The proposed methodology starts with a data preparationstage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables ifnecessary. The feature analysis technique was used to select the most important features for the students academic performance model. A number ofmachine learning classifiers were tested, and the feature analysis was found to be the best performer. The results of this study demonstrate the potential of algorithms in predicting student performance andidentifying key factors that influence academic success. This information can be leveraged by educators and academicinstitutions to develop targeted intervention strategies, tailoredlearning experiences, and personalized recommendations forstudents, ultimately fostering a more effective learningenvironment and improving overall educational outcomes.
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References
Zachi I. Attia, Paul A. Friedman, Peter A. Noseworthy, Francisco Lopez-jimenez, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter , Suraj Kapa Age And Sex Estimation Using Artificial Intelligence From Standard 12-lead Ecgs
Levi, G., &Hassner, T. (2015) Age and Gender Classification Using Convolutional Neural Networks. In Proceedings of The Ieee Conference On Computer Vision And Pattern Recognition Workshops
Khalaf, O.I., Abdulsahib, G.M., &Sadik, M. (2018) A Modified Algorithm For Improving Lifetime Wsn.
Cole, J.H., Poudel, R.P., Tsagkrasoulis, D., Caan, M.W., Steves, C., Spector, T.D., & Montana, G. (2017) Predicting Brain Age with Deep Learning From Raw Imaging Data Results In A Reliable And Heritable Biomarker. Neuroimage, 163, Pp. 115-124.
Antipov, G., Baccouche, M., Berrani, S.A., &Dugelay, J.L. (2017) Effective Training of Convolution Neural Networks For Face-based Gender and Age Prediction.
Grassmann, F., Mengelkamp, J., Brandl, C., Harsch, S., Zimmermann, M.E., Linkohr, B., & Weber, B.H. (2018) A Deep Learning Algorithm For Prediction Of Age-related Eye Disease Study Severity Scale For Age-related Macular Degeneration from Color Fundus Photography.
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A (2016) Deep Neural Networks Predict Hierarchical Spatio-temporal Cortical Dynamics Of Human Visual Object Recognition.