IDEAS home Printed from https://ideas.repec.org/a/zna/indecs/v18y2020i2p175-193.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://indecs.eu/2020/indecs2020-pp157-193.pdf
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bergeaud, Antonin & Raimbault, Juste, 2020. "An empirical analysis of the spatial variability of fuel prices in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 131-143.
    2. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    3. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
    4. Castellares, Fredy & Patrício, Silvio C. & Lemonte, Artur J. & Queiroz, Bernardo L., 2020. "On closed-form expressions to Gompertz–Makeham life expectancy," Theoretical Population Biology, Elsevier, vol. 134(C), pages 53-60.
    5. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    6. Huan Yu & Jun Yang & Yu Zhao, 2018. "Reliability of nonrepairable phased-mission systems with common bus performance sharing," Journal of Risk and Reliability, , vol. 232(6), pages 647-660, December.
    7. Muhammet Burak Kılıç & Yusuf Şahin & Melih Burak Koca, 2021. "Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation," Computational Statistics, Springer, vol. 36(2), pages 1219-1242, June.
    8. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    9. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    10. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    11. Imbert, Clément & Papp, John, 2020. "Costs and benefits of rural-urban migration: Evidence from India," Journal of Development Economics, Elsevier, vol. 146(C).
    12. Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
    13. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
    14. Fabio Blasutto & David de la Croix, 2023. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," The Economic Journal, Royal Economic Society, vol. 133(656), pages 2899-2924.
    15. Maarten J. Punt & Brooks A. Kaiser, 2021. "Seismic Shifts from Regulations: Spatial Trade-offs in Marine Mammals and the Value of Information from Hydrocarbon Seismic Surveying," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(3), pages 553-585, November.
    16. Schwamborn, R. & Mildenberger, T.K. & Taylor, M.H., 2019. "Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN," Ecological Modelling, Elsevier, vol. 393(C), pages 37-51.
    17. Teodora Basile & Antonio Maria Amendolagine & Luigi Tarricone, 2022. "Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model," Agriculture, MDPI, vol. 13(1), pages 1-11, December.
    18. Nikolaos Nagkoulis & Konstantinos L. Katsifarakis, 2022. "Using Game Theory to Assign Groundwater Pumping Schedules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1571-1586, March.
    19. Shukla, Abhinek & Grover, Rhythm & Kundu, Debasis & Mitra, Amit, 2022. "Approximate least squares estimators of a two-dimensional chirp model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    20. Finn Olesen, 1999. "Monetær integration i EU," Working Papers 2/99, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zna:indecs:v:18:y:2020:i:2:p:175-193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Josip Stepanic (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.