Data shows: scale also affects study efficiency in higher vocational education
Data shows: scale also influences study efficiency in higher vocational education
What do the data tell us about the quality of education and study efficiency in higher vocational education? What tools do the data offer for improving this? Open data on higher vocational education has been collected, analyzed and visualized in a dashboard. This dashboard can be found on our website.
From the analysis it can be seen that study efficiency after five years of education in HBO has decreased by about ten per cent points since 2002. Whereas almost sixty per cent of the students who started their studies in 2002 (the so-called 2002 cohort) graduated in five years, this is only about fifty per cent for students who started ten years later.
For universities, the smaller ones do better than the larger ones. This is also true for colleges of higher education. The dashboard shows that large institutions (with more than 20,000 students) score below the national average (the black line in the figure) on study efficiency. However, the gap with the national average has narrowed substantially since the 2000 cohort.
To better understand the relationship between student success and institution size, a correlation analysis was performed.
This clearly shows that the small institutions are doing better than the large institutions: the small institutions are in the top left of the figure and the large ones in the bottom right.
One explanation for the better performance of small institutions may be that they offer very specific degree programs (especially in the arts, agri-business and based on a specific foundation or signature).
Students who choose these institutions are thus making a more conscious choice for a specific program. In academic education, this can be seen at Wageningen University. This university has come out on top in the Study Choice Guide every year since 2004. Students who choose this university in "provincial village" Wageningen must be "very motivated," according to the guide.
Learning analytics is the basis for interventions to increase yield and quality
So scale also has a negative effect on student yield in higher vocational education. What can colleges do with these insights? Radically instituting a student stop is not desirable in terms of accessibility of education and finances. However, several interventions can be considered. Large institutions can start educating students in a much more targeted way, especially students who want to opt for the large programs. Another possible intervention is the introduction of admission tests, which also address motivation. With the help of learning analytics techniques, large institutions can monitor their students much better and support them earlier if necessary. Also, large institutions can handle recruitment differently. Here, too, the comparison with academic education is relevant: universities that have relied too much on successful city marketing (especially the University of Amsterdam and Erasmus University) are now reaping the bitter fruits of this in the form of lower study yields.
With the help of learning analytics, numerous interventions can be developed that can increase educational quality and study efficiency. Examples include offering the opportunity to study on a trial basis, active study career guidance, better teaching of study skills, creating a learning community through learning groups, using fellow students as peer mentors and using formative assessment to stimulate students to learn actively and continuously. For better individual guidance, very specific target groups can be selected that are doing particularly poorly or well. The students in these groups can then be provided with much more customized services by an institution. Another possibility is to predict dropouts and respond accordingly. This requires data at the level of the individual student. Data that is not (thankfully) available as open data, but which institutions themselves have at their disposal.