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Hybridization of cluster-based LDA and ANN for student performance prediction and comments evaluation

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Abstract

For a productive life, education plays a critical role to fill individual life with value and excellence. Education is compulsory to provide things that individuals partake in to compete in the modern world. Predicting the academic performance of the student is the most successive research in this era. A different set of approaches and methods are incorporated to increase student performance. However, this is a challenging task due to the wrong course selection. In the proposed study, we have used the hybrid approach consisting of Cluster-based Linear Discriminant Analysis (CLDA) and Artificial Neural Network (ANN) to provide the prospective students with the motivational comments and the video recommendations by which students can choose the right subject and the comments will facilitate the students with the insight reasons of dropout opted by other students for this course. The outcomes of this study will help in the reduction of the number of dropouts. The students will be able to choose an appropriate course for performance enhancement and carrier excel.

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Notes

  1. https://www.kaggle.com/chellaindu/mooc-dataset/data?select=cs_mitx.csv

  2. https://www.mockaroo.com/

  3. https://www.openml.org/a/estimation-procedures/7

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Authors and Affiliations

  1. Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India

    Sakshi Sood & Munish Saini

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  1. Sakshi Sood
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  2. Munish Saini
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Correspondence to Sakshi Sood.

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Sood, S., Saini, M. Hybridization of cluster-based LDA and ANN for student performance prediction and comments evaluation. Educ Inf Technol 26, 2863–2878 (2021). https://doi.org/10.1007/s10639-020-10381-3

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  • DOI: https://doi.org/10.1007/s10639-020-10381-3

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