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Analysing Computer Science Courses over Time

Author

Listed:
  • Renza Campagni

    (Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy
    These authors contributed equally to this work.)

  • Donatella Merlini

    (Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy
    These authors contributed equally to this work.)

  • Maria Cecilia Verri

    (Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy
    These authors contributed equally to this work.)

Abstract

In this paper we consider courses of a Computer Science degree in an Italian university from the year 2011 up to 2020. For each course, we know the number of exams taken by students during a given calendar year and the corresponding average grade; we also know the average normalized value of the result obtained in the entrance test and the distribution of students according to the gender. By using classification and clustering techniques, we analyze different data sets obtained by pre-processing the original data with information about students and their exams, and highlight which courses show a significant deviation from the typical progression of the courses of the same teaching year, as time changes. Finally, we give heat maps showing the order in which exams were taken by graduated students. The paper shows a reproducible methodology that can be applied to any degree course with a similar organization, to identify courses that present critical issues over time. A strength of the work is to consider courses over time as variables of interest, instead of the more frequently used personal and academic data concerning students.

Suggested Citation

  • Renza Campagni & Donatella Merlini & Maria Cecilia Verri, 2022. "Analysing Computer Science Courses over Time," Data, MDPI, vol. 7(2), pages 1-15, January.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:2:p:14-:d:732383
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    References listed on IDEAS

    as
    1. Antonio Hernández-Blanco & Boris Herrera-Flores & David Tomás & Borja Navarro-Colorado, 2019. "A Systematic Review of Deep Learning Approaches to Educational Data Mining," Complexity, Hindawi, vol. 2019, pages 1-22, May.
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