Abstract
In the present educational system, student performance prediction is very useful. Predicting student performance in advance can help students and their teacher to track the performance of the student. Many institutes have adopted continuous evaluation system today which is done manually. Such systems are beneficial to the students in improving performance of a student. In data mining applications, it is seen that neural networks are widespread and has many successful implementations in a wide range. The goal is to know whether neural networks are right classifiers to predict the student performance in the domain of educational data mining. Neural network surpass many algorithms which are tested on particular dataset and can be used for successful prediction of student performance. Classification is used as a popular technique in predicting student performance. Several methods are used under the classification such as decision tree, naïve bayes tree, support vector system, k nearest neighbor, random forest and logistic regression.
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Acknowledgements
We thank center of intelligence Artificial Intelligence and Deep Learning, Department of computer science and engineering, CMRCET for the support and especially thankful to our project coordinator Dr M.D. Ansari whenever needed.
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Swathi, M., Soujanya, K.L.S., Suhasini, R. (2021). Review on Predicting Student Performance. In: Kumar, A., Mozar, S. (eds) ICCCE 2020. Lecture Notes in Electrical Engineering, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-15-7961-5_120
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DOI: https://doi.org/10.1007/978-981-15-7961-5_120
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