IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i2p14-d732383.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/2/14/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/2/14/
    Download Restriction: no
    ---><---

    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.
    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. Aras Bozkurt & Abdulkadir Karadeniz & David Baneres & Ana Elena Guerrero-Roldán & M. Elena Rodríguez, 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    2. Hülya Yürekli & Öyküm Esra Yiğit & Okan Bulut & Min Lu & Ersoy Öz, 2022. "Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    3. Tieyuan Liu & Chang Wang & Liang Chang & Tianlong Gu, 2022. "Predicting High-Risk Students Using Learning Behavior," Mathematics, MDPI, vol. 10(14), pages 1-15, July.

    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:gam:jdataj:v:7:y:2022:i:2:p:14-:d:732383. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.