Abstract
The goal of educational institutions is to enhance the quality of education and uplift students’ academic performance. To attain the highest level of quality, experts need to find out the most influential features affecting academic performances and must attempt to solve weakness of those features. Educational data mining (EDM) tools provide the best measure to achieve so. For educational datasets, quality of prediction results can be upgraded by using feature selection (FS) procedures. Unrelated data and attributes present in dataset can be removed by using proper FS methods which will result in more accurate results in EDM practices. Performance of prediction results can be enhanced by choosing appropriate dataset and attributes. This paper utilizes educational data mining approach through a few chosen feature selection procedures and machine learning algorithms to predict students’ final grade. Obtained results attempt to find out influence of different student-related attributes in the prediction.
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Mitra, A., Decosta, A., Roychoudhury, N., Acharya, A. (2022). Students Performance Prediction Using Educational Data Mining. In: Dahal, K., Giri, D., Neogy, S., Dutta, S., Kumar, S. (eds) Internet of Things and Its Applications. Lecture Notes in Electrical Engineering, vol 825. Springer, Singapore. https://doi.org/10.1007/978-981-16-7637-6_16
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DOI: https://doi.org/10.1007/978-981-16-7637-6_16
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