Consent, Transparency and Access: How to Use Learning Analytics Ethically

Christina Gómez
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London, United Kingdom

Internet and software users often use tools without properly reading the terms and conditions or thinking about the social repercussions these might have. In teaching and learning applications that use user data, this can be an important and sensitive topic. One of the tools currently prone to skepticism and ethical issues is learning and predictive analytics.

Matt Lingard is the Head of Technology-Enhanced Learning at the University of West London, in England, and as soon as the university decided to implement a learning analytics program, he considered it was essential to also apply a policy of use to go along with it. Some of the reasons why analytics generates an ethical debate is due to the possibility a negative prediction on a student might have repercussions on his or her feelings towards a course, influence the teacher’s grade, or make the student seem less of an individual and more of a statistic. It’s important to understand how to use analytics to create a code of conduct that supports teachers to make appropriate use of the insights learning analytics can provide.


The entrance of the University of West London. Foto: AFP Glyn Kirk.
The entrance of the University of West London. Foto: AFP Glyn Kirk.

Learning analytics are a set of data, measurements, and analysis of different sources of information such as student demographics, use of Blackboard Learn as an LMS, and student record systems, that show the student’s progression through a course and the different topics they might have trouble with. Analytics helps faculty to reach out to at-risk students and offer help where needed in order to prevent dropouts.

Learning analytics works with Big Data, which is a set of algorithms and statistics that can mathematically add up certain components and make conclusions based on them. For example, the amount of times a student enters Blackboard Learn to see his/her classes, assignments, and feedback can show high engagement. That, along with high grades but a low in-person attendance rate, might show that the student wishes to continue studying but is unable to attend classes for some reason. The predictive analytics tool used at the University of West London examines whether a student is likely to continue studying the following semester. It produces a ‘persistence prediction’ based on the analysis of the student’s scores, attendance, demographic data and engagement with the LMS. Since all this data is very important, but also very sensitive, the University of West London published a policy based on 10 principles to have in mind when using learning analytics.

Three of the 10 principles are particularly important and, as Matt explains, all intertwine with the each other. These 3 principles are:

  1. Consent is one of the key areas of the policy, and its informed consent more than anything. When students enroll in any university, they are giving consent to the university to use their information. However, due to the a sensitive nature of this topic, the objective of why and how learning analytics data is being used must be clear. Students must understand that analytics is used for their benefit and to improve overall teaching and learning at the institution. At University of West London, the implementation of learning analytics has been gradual and only certain areas of the university are currently using analytics: the School of Computing and Engineering and the London Geller College of Hospitality and Tourism. These two schools are the first to use analytics, since the university wants to ensure that all staff are properly trained before implementing it in all faculties.
  2. Transparency and openness are both policy pillars and are tied to consent. The university aims to be transparent so students know exactly how their information is going to be used. This is one area that Matt acknowledges requires some more work from the university and a more proactive campaign is needed.
  3. Access is an important reason as to why the policy was created in the first place. It’s important to keep restricted access to the data and is only seen by those who need to see it. In the UK, there is a personal tutor role. Each personal tutor is assigned 20 students and supports them in academic matters. The personal tutor has all the information related to predictive analytics, their attendance records and demographics in order to have informed conversations with their students. There is also a bigger set of analytics available for the student engagement and senior management teams, which includes analytics for the school’s population as a whole, and not just for the individual student. This will allow the university to make strategic decisions based on insights for particular groups of students. For example, first year students versus mature students, and so forth.
It’s important to understand how to use analytics to create a code of conduct that supports teachers to make appropriate use of the insights from learning analytics.

Implementing this policy was important in order to ensure analytics doesn’t interfere or influence grading, and that individuals’ data privacy remains intact. This means understanding that there will be students who do not follow the typical patterns and will not turn out as anticipated by predictive analytics.

In order for this policy to be written, Matt led a team of 12 people who wholly represented the university: teaching staff, student body representation, senior management, the Pro-Vice Chancellor for Education, and the University Secretary (the person responsible for the university’s legal matters). After some research, Matt realized that it was simple to create a new policy that was modeled on Open University’s existing policy – in place for three years already- along with the advice that the Jisc (Joint Information Systems Committee) provided. Matt explains that he didn’t see the point in reinventing the wheel when he had two superb fountains of information which additionally had a creative commons license.

Matt also expressed that all the work associated with creating the policy was completely worth it because of the benefits that a learning analytics program can do for the students’ lives. The whole purpose of analytics is to use data to help ensure student success and support at-risk students in danger of dropping out.

Overall, learning analytics is a great tool with huge advantages, which, like all things, has to be used wisely, responsibly, ethically, and with full consent of everyone involved. The University of West London is a sound example for any educational institution planning to implement or enhance their own analytics program.


*Matt Lingard, Head of Technology and Enhanced Learning.

*Photos by: AFP Glyn Kirk

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