Abstract
Institutions of higher learning operate in a highly competitive environment. To compete with world-class institutions, institutes must adapt their strategy to increase overall performance. Academic achievement of students is one of the most important factors in improving an institution's ranking and recognition. Performance of students in an academic program depends upon several aspects of their previous academic performance and family background. In the present study, an artificial neural network (ANN) is developed using Python programming language to predict students’ performance and to determine the outcome of students’ performance. Students’ data were collected through a questionnaire-based survey from postgraduate students of technical education institutions all over India. The appropriate ANN model is identified, and the Python code for the same is developed with the help of Keras library. The developed model did not have the expected accuracy due to lack of adequate number of responses required for deep learning techniques, but still valuable results are obtained such as that of identifying some crucial factors.
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Varun, M., Sridharan, R., Eldose, K.K. (2023). Academic Performance Prediction of Postgraduate Students Using Artificial Neural Networks. In: Vučinić, D., Chandran, V., Mahbub, A.M., Sobhan, C.B. (eds) Applications of Computation in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-6032-1_20
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DOI: https://doi.org/10.1007/978-981-19-6032-1_20
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