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      Improving Students’ Retention Using Machine Learning: Impacts and Implications

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      In review
      research-article
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      ScienceOpen Preprints
      ScienceOpen
      Data Mining, Retention, Machine Learning, Neural Networks, SVM
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            Revision notes

            The following revision has been made.  

            1.  Write a new abstract and introduction.

            2.  Figures were fine-tuned and research work was included   

             

            Abstract

            Traditional statistical tools and qualitative techniques were employed in the literature to discover and forecast charac teristics/factors that impact student retention the most. Modeling the links between these early available indicators and a student's future status of engineering persistence can be very useful in improving student retention in engineering. For some years, machine learning approaches have been used in education to predict retention and discover factors impacting retention rates, with better outcomes since 2010. This study focuses on different machine learning techniques used in literature for improving students’ retention; we have identified various factors that might affect the students’ retention and employed SVM and Neural Networks for predicting students’ retention rates.

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            13 August 2022
            Affiliations
            [1 ] Member of IEEE.org, Technocrats Institute of Technology, MP, India
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPZMB0B.v2
            9bd54fb9-1b77-4fa7-9a9d-0a88763c5922

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Assessment, Evaluation & Research methods,Applied computer science
            Retention,Neural Networks, Machine Learning,SVM,Data Mining

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