Higher Education

, Volume 76, Issue 6, pp 1007–1025 | Cite as

A probabilistic approach to student workload: empirical distributions and ECTS

  • Antonio Souto-Iglesias
  • María Teresa Baeza_RomeroEmail author


The ECTS, European Credit Transfer System, is now widely used throughout higher education institutions as it facilitates student mobility within Europe and the comparison of study programs and courses. Most European institutions provide students with the number of ECTS each course and module is worth. A full-time student needs to complete 60 ECTS per academic year, which represents about 1500 to 1800 h of study. However, there is a lack of research showing that ECTS metrics have been properly implemented in different degrees and universities. The aim of this paper is to assess the relevance of the ECTS metric as a valid indicator of students’ and courses’ workloads. Detailed workload measurements have been taken in two Spanish universities, with 250,000 work hours monitored from 1400 students. This is the first study published with such a large dataset that includes a range of simultaneous courses and throughout a whole semester. Empirical distribution functions of workload indicators have been obtained. Evidence is provided indicating that nominal ECTS credit hours may be overestimated, that the variability of student workload could be too large for ECTS to sensibly characterize course workload, and that workload statistics of courses with same nominal ECTS are generally not comparable. Although the ECTS metric conception seems to be a valid metric to facilitate mobility between different institutions and higher education systems, in practice, according to this study, it requires revision, at least in the two institutions that have been included in this study. Further studies like the present one are required to test if this is a broader problem that has implications for the comparability of degrees across Europe.


Student workload Workload monitoring ECTS (European Credit Transfer System) EHEA (European Higher Education Area) Workload ECDF (Empirical Cumulative Distribution Function) 



We want to thank all students and lecturers involved in this study.

Funding information

This work has been funded in part by the Education Innovation Projects call 2011 by Universidad Politécnica de Madrid and the 8th Education Innovation Projects call of the Universidad de Castilla-La-Mancha.


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018
corrected publication May/2018

Authors and Affiliations

  1. 1.CEHINAV, DMFPA, ETSINUniversidad Politécnica de Madrid (UPM)MadridSpain
  2. 2.Escuela de Ingeniería Industrial de ToledoToledoSpain

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