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Supporting academic decision making at higher educational institutions using machine learning-based algorithms

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Abstract

Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented.

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Authors and Affiliations

  1. Department of Computer Science, University of Oviedo, Calle Federico García Lorca, Oviedo, Spain

    Yuri Nieto & Vicente García-Díaz

  2. District University Francisco José de Caldas, Carrera 7 #40b-53, Bogotá, 1100100, Colombia

    Carlos Montenegro

  3. School of Engineering, Universidad Internacional de La Rioja (UNIR), Logroño, Spain

    Rubén González Crespo

Authors
  1. Yuri Nieto
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  2. Vicente García-Díaz
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  3. Carlos Montenegro
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  4. Rubén González Crespo
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Corresponding author

Correspondence to Rubén González Crespo.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Nieto, Y., García-Díaz, V., Montenegro, C. et al. Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Comput 23, 4145–4153 (2019). https://doi.org/10.1007/s00500-018-3064-6

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  • DOI: https://doi.org/10.1007/s00500-018-3064-6

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