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Genetic algorithms as a tool for development of balanced curriculum

Author

Listed:
  • Fuad Dedic

    (University of Dzemal Bijedic in Mostar - Faculty of Information Technologies, Mostar, Bosnia and Herzegovina)

  • Nina Bijedic

    (University of Dzemal Bijedic in Mostar - Faculty of Information Technologies, Mostar, Bosnia and Herzegovina)

  • Drazena Gaspar

    (University of Mostar - Faculty of Economics, Mostar, Bosnia and Herzegovina)

Abstract

The article presents research about the use of genetic algorithms in the analysis of the interrelation among curriculum courses in higher education. The authors used genetic algorithms as a method to analyse the influence that achieved grades in predictors' courses have on achieved grades in dependent courses as well as to observe whether the genetic algorithms can contribute to improving the curriculum. The research was based on a set of data related to the success of students from the Faculty of Information Technologies at the University 'Džemal Bijediæ' in Mostar, Bosnia and Herzegovina. The aim was to anticipate students' grades based on the grades they obtained in previous semester's courses. This research should help educational institutions to evaluate the suitability of the sequence of courses within the curriculum in order to enable personalized learning paths, make the teaching processes more efficient, and promote a balanced curriculum. Namely, a good curriculum can attract new students, improve the success rate of enrolled students, and increase the quality and visibility of the institution. Since the genetic algorithm is search techniques for handling complex spaces, we can use it for the research at each stage of the educational process. Analyses of quantitative data using a genetic algorithm can help educational institutions improve the quality of teaching.

Suggested Citation

  • Fuad Dedic & Nina Bijedic & Drazena Gaspar, 2020. "Genetic algorithms as a tool for development of balanced curriculum," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(2B), pages 175-193.
    • Handle: RePEc:zna:indecs:v:18:y:2020:i:2:p:175-193
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    File URL: http://indecs.eu/2020/indecs2020-pp157-193.pdf
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    References listed on IDEAS

    as
    1. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
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    More about this item

    Keywords

    balanced curriculum; curriculum evaluation; genetic algorithm; personalized learning;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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