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Improving the evaluation process of students’ performance utilizing a decision support software

  • EANN 2017
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

During the last decades, educational data mining constitutes a significant tool, offering a first step and a helping hand in the prediction of students’ progress and performance. In this work, we present a user-friendly decision support software, for accurately predicting the students’ performance at the final examinations of the academic year. The proposed software incorporates a classification scheme which has two major features. Firstly, it identifies with high accuracy the students at-risk of failing the final examinations; secondly, it classifies the students based on their predicted grades. Our numerical experiments show that it achieves better performance than any examined single learning algorithm. The proposed software was developed to provide assistance to students’ evaluation and mostly to the early identification of students’ at-risk in order to take proper actions for improving their performance.

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Notes

  1. The tool is available at http://www.math.upatras.gr/livieris/DSSPSP.zip. Notice that Java Virtual Machine (JVM) 1.2 or newer is needed for the execution of the program.

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Acknowledgements

The authors are grateful to the Microsoft showcase school “Avgoulea-Linardatou” for the collection of the data used in our study, for the evaluation of the tool and for their kind and valuable comments, which essentially improved our work.

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Authors and Affiliations

  1. Department of Computer and Informatics Engineering (DISK Lab), Technological Educational Institute of Western Greece, 263-34, Antirrion, Greece

    I. E. Livieris & V. Tampakas

  2. Department of Business Administration (LAIQDA Lab), Technological Educational Institute of Peloponnese, 241-00, Kalamata, Greece

    T. Kotsilieris

  3. Department of Mathematics, University of Patras, 265-00, Patra, Greece

    P. Pintelas

Authors
  1. I. E. Livieris
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  2. T. Kotsilieris
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  3. V. Tampakas
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  4. P. Pintelas
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Correspondence to I. E. Livieris.

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The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Livieris, I.E., Kotsilieris, T., Tampakas, V. et al. Improving the evaluation process of students’ performance utilizing a decision support software. Neural Comput & Applic 31, 1683–1694 (2019). https://doi.org/10.1007/s00521-018-3756-y

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