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Predicting Pupil’s Successfulness Factors Using Machine Learning Algorithms and Mathematical Modelling Methods

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Advances in Computer Science for Engineering and Education II (ICCSEEA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 938))

Abstract

Taking into account the challenges and problems that are faced by the modern educational process, it is considered to use modern intelligent systems and algorithms to improve the education and teaching levels in educational institutions. The article describes an algorithm of actions on machine learning using, determining the students success level and analyzing the obtained data. This research can be efficiently used to find out and detect the modern educational problems, and individual and collective pupils sample features, implement the classification process and regression analysis of the data set. Results obtained from the algorithms usage, data analysis are described and demonstrated. The main features, knowledge and insights obtaining methods from the dataset are determined. The applied method is quite efficient and is capable of assessing pupil’s performance metrics. Predicting student’s and pupil’s characteristics will help to segment and divide them into different classes so that it will allow pupils to develop communication, leadership, and self-management skills while studying at school or university. The results show that performance metrics assessment is an integral part of modern education process that is slightly crucial for its improvement and pupil’s trends in education exploration.

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Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

    Solomia Fedushko & Taras Ustyianovych

Authors
  1. Solomia Fedushko
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  2. Taras Ustyianovych
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Corresponding author

Correspondence to Solomia Fedushko .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. “Igor Sikorsky Kiev Polytechnic Institute”, National Technical University of Ukraine, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, FL, USA

    Matthew He

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Fedushko, S., Ustyianovych, T. (2020). Predicting Pupil’s Successfulness Factors Using Machine Learning Algorithms and Mathematical Modelling Methods. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_58

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