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.
