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Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India

Research Article

Machine Learning-based Model to Predict Student's success in Higher Education

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  • @INPROCEEDINGS{10.4108/eai.24-3-2022.2318766,
        author={Atul  Garg and Nidhi Bansal Garg and Umesh Kumar Lilhore and Renu  Popli and Sarita  Simaiya and Ankit  Bansal},
        title={Machine Learning-based Model to Predict  Student's success in Higher Education},
        proceedings={Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India},
        publisher={EAI},
        proceedings_a={ICIDSSD},
        year={2023},
        month={5},
        keywords={naive bayes k* students performance hei's machine learning big data},
        doi={10.4108/eai.24-3-2022.2318766}
    }
    
  • Atul Garg
    Nidhi Bansal Garg
    Umesh Kumar Lilhore
    Renu Popli
    Sarita Simaiya
    Ankit Bansal
    Year: 2023
    Machine Learning-based Model to Predict Student's success in Higher Education
    ICIDSSD
    EAI
    DOI: 10.4108/eai.24-3-2022.2318766
Atul Garg1, Nidhi Bansal Garg1, Umesh Kumar Lilhore1,*, Renu Popli1, Sarita Simaiya1, Ankit Bansal1
  • 1: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
*Contact email: umesh.lilhore@chitkara.edu.in

Abstract

Predictions are always helpful for making decisions. Students are the future of the world. Higher Education Institutions (HEI's) in developing countries cannot apply similar strategies to all the students. Academic achievement plays a crucial role in the academic system because it is often utilized for the educational establishment quality. Early identification of at-risk educators and prevention strategies can significantly improve their chances of succeeding. Education is affected by different environments, family backgrounds, social and personal responsibilities. In this research article students, performance is measured based on various parameters using Random Forest, Naive Bayes and K* method. Experimental analysis shows the strengthening of the random forest method over K* and Naive Bayes method.

Keywords
naive bayes k* students performance hei's machine learning big data
Published
2023-05-16
Publisher
EAI
http://dx.doi.org/10.4108/eai.24-3-2022.2318766
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