We can probably all recall a situation involving you, your phone with GPS when a sudden personalized advertisement pops up, offering some of “the best offers” corner away from your current location. Similarly to education, as a student clicks, swipe and tap, they are leaving a footprint behind which is a relevant data to analyze for an organization. With the data available, the confusion at the policy level is reflected at the personal level. It is clear that students have concerns about privacy and about the potential for their information to be tracked and be used further. In addition, what is the main issue behind using student's data to improve education?
Higher education institutions have always used a variety of data about students. An example would be socio-demographic information, grades on higher education entrance qualifications, or pass and fail rates, to inform their academic decision-making as well as for resource allocation. However, while living in the digital age somehow privacy issue has become a very sensitive topic. With your permission, we would like to outline what we think is the main problem.
Higher education institutions have always used a variety of data about students.
Below, we illustrated the three main steps in data science. While having the example of a student in mind, in the first step, data gathering collects all data about a person such as his location or academic performance. In the second step, the process of learning analytics is happening to make a tailored advice or an personal offer to the student. Once we reach the last stage, presenting, the person receives the outcome of the analysis (such as the tailored study advice or where is the nearest supermarket with your favourite chocolate in sale).
In addition, in the first step, you are asked to agree with your data being used. One might not be aware of passing this stage, since it often consists of a rather long privacy statement while launching the software that requires you to only tick a box “I agree”. In the second step a student or a regular user is not involved and therefore by the time he reached the last step, he might experience a slight shock how “smart” the software is tailor the advice to him only. While zooming out of the topic for a while, from our point of view, step 3 causes the concerns in students and raises the question: “How did I get to this stage?”
Creating trust of students and staff is essential to institutions
Creating trust of students and staff is essential to institutions, and the protection of privacy should not been seen as a burden. Instead, it should be seen as a valuable service that can be offered to build a trusting relations between the user and learning analytics or any software. To improve this, students should be guided through each step of the analysis and be appropriately informed about the stages that their data will go through. When saying appropriately informed, one of the actions taken could be avoiding long privacy statements. Instead, understandably explain or highlight what will happen with the users data while agreeing. This can improve the perception of learning analytics in general, so that instead of perceiving it as a control of data, see it as a tool help.
Privacy and ethics in learning analytics have their own dynamic, and are informed by the traditions of education. However, their future will surely be strongly informed by the wider ethical, political, economic and technological debate. By showing transparency in the steps of data collection would prevent many misconceptions. It would also create trust between the user and analyser. And not to forget, it will surely prevent your surprised face while receiving a pop up ad next time.