Analysing Computer Science Courses over Time

Renza Campagni, Donatella Merlini and Maria Cecilia Verri
Additional contact information
Renza Campagni: Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy
Donatella Merlini: Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy
Maria Cecilia Verri: Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, I-50134 Firenze, Italy

Data, 2022, vol. 7, issue 2, 1-15

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.

Keywords: course analysis; clustering; classification; heat maps (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/7/2/14/pdf (application/pdf)
https://www.mdpi.com/2306-5729/7/2/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:7:y:2022:i:2:p:14-:d:732383

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().