Project introduction and background information
"Knowledge is Power”, within our various administrative and learning systems, we have a lot of data on our students and their ‘study behaviour’. So far, WU is doing very little with these data. ‘Learning Analytics’ is a rapidly developing field[1] in which various types of data are combined to gain more insight in how to make the learning of students most effective/efficient/successful. We will explore the use of learning and teaching analytics at Wageningen University and when successful, use this to use an evidence based/data driven approach to education efficiency improvement in the years to follow. Currently, there is no experience with quantitative learning analytics for our BSc/MSc courses. Some sporadic data on limited variables exist (e.g. showing up at statistics tutorials and final grade), but that is about it. Within this project, we start looking at the data without first setting up a hypothesis, this is sometimes called ‘a fishing expedition’. This prevents us from missing surprising potential effects, we’re open for serendipity. Based on hypotheses from the identified correlations, where necessary complemented with (underlying) hypotheses that are commonly used in teaching at WU, the data will be analysed, courses will be looked into and project conclusions will be drawn and discussed with the course coordinator.
[1] Personal observations at LAK’16, Educause2016 and ‘Onderwijsdagen 2016’
Objective and expected outcomes
Improving education efficiency by an evidence based/data driven approach.
- We expect that effects (significant or nearly so) can be observed so that they can be used in a more evidence based approach to teaching at Wageningen University.
- With learning analytics, students might be able to ‘benchmark’ their own learning behaviour and see if they are on their right track (e.g. to pass with an above average grade). They can get a more personalized indication on their progress on this route