The following post was written by Johannes Schmiedecker, a BILT Student Fellow.
In early April, the BILT Student Fellows conducted various workshops at the Bristol SU Education Network. Below are some findings from the workshop about learning analytics in the HE sector.
8 groups of students (maximum 7 people per group) had to decide if the University should or should not include various metrics when it processes student data to improve the university life. The metrics were written down on single index cards and included different data, ranging from academic data such as assessment grades, blackboard access or library usage to personal data like gender, religion or ethnicity. The groups had to decide collectively and only allocate a certain data metric to either “Yes” or “No” if all the members of the group agreed. After 10 minutes the time ended, and students were asked to reflect on the task.
The groups engaged in active discussions and we saw that students had different opinions when it comes to finding an accurate balance between privacy rights and data analytics. Students were mostly open to providing their data for learning analytics as they saw that it can improve university life, however, they expect clear policies and strategies from the University before they would agree to such a thing.
The following bar chart provides a summary of how the students allocated the cards. For instance, all 8 groups said that attendance in classes or assessment grades data should definitely be included in learning analytics. However, when it came to more personal data like the current employment situation, gender or ethnicity, the results were mixed, and some groups could not agree to either Yes or No. Furthermore, all groups decided that facial recognition at campus or comments on social media should be excluded. In general, the groups could allocate academic data easier, agreeing on a usage of personal data was far more contentious.
As the time of the workshop was limited, the findings do not provide a full picture of the issue of data analytics, but it was good to get some student feedback and listen to their approach to data usage at the HE level. After the workshop the students had the chance to express their thoughts on the workshop, and the responses varied. “We kind of mulled over each metric, it was hard to decide!” one student said. Some were generally critical towards data analytics, “The question is, how the University is going to use the data? What do they want to do with it and why? It really depends on how the Uni uses it!” was one response. Others had a more open attitude towards data analytics and were fine with the usage of their data if it was education based and the university processed the data in a transparent way. “It would be nice if the University had an opt-out policy, if there are tick boxes and we could decide which data we want to provide. This would be the best way to approach it because everyone has different opinions!” one student argued who advocated for more control of students over their own data.
It was great to hear so many different opinions on how the University should use data of students. It demonstrated that there are many perspectives on how to approach learning analytics and a University policy would need to consider many different aspects.
Thanks to all participants, we are looking forward to the next BILT workshops and activities!