Ethical Issues with Learning Analytics
February 8th, 2018
In a digitally-fueled education landscape, learning analytics plays an instrumental role in helping institutions gain a nuanced understanding of learner needs and use it to shape their learning & progression. Analyzing data relating to learners and their engagement with course curriculum lies at the heart of this process. Learner data is useful for all stakeholders – the individual, teachers, administrators, etc. However, its collection and use
The amount of personal data that the Learning Management Systems (LMS) makes available is unprecedented with hardly any established ethical standards that the educators are required to follow. Once this data becomes available publicly or is compromised, individuals lose control of their data and who gets to use it.
Read more: User Learning Analytics
Challenges faced with Learning Analytics
Let’s take a look at some of the most pressing ethical issues with learning analytics today:
- Privacy: Questions like who has access to data, if it is in safe hands, and how students can access their data are pertinent to the privacy concern of learning analytics. Moreover, who holds access to the data that’s collected and whether administrators have the same access as instructors are also important factors. If learners are geographically scattered, instructors would want to know the city they live in, but would that mean they would have details down to their street address? Other sensitive information, such as credit card, SSN, passwords, etc. should likely be private as well.
- Location of data: Data distributed across geographic locations makes it difficult to track or keep it secure. Since a significant amount of learner activity takes place outside of the institution, records are distributed across a variety of sites, owners, and levels of access. This makes it difficult to enforce a single set of guidelines pertaining to ethical use across locations, with each carrying its data protection standards.
- Data misinterpretation and misrepresentation: Since data analytics is based on comprehensive analysis and interpretation, any sort of inaccuracies can lead to misrepresentation of data. For instance, in the event of missing or incomplete data related to an LMS, the correlation between different variables may be misunderstood. These assumptions could be influenced by the analyst’s prejudices, resulting in subconsciously-biased interpretations.
- Learner consent: Although students are becoming increasingly aware of data mining to monitor behaviour, the extent to which this occurs in an educational setting is an aspect they aren’t familiar with. Instructors then need to decide how much should be disclosed to students from the information collected and what is going to be its eventual use. Can some or all of their data be used without consent? While there’s general opinion that education providers are ethically obligated to get student sign-offs, there isn’t an established standard of what that should include.
- De-identification of data: It’s important that administrators retain unique identifiers for individual learners without knowing their actual identity. Considering the growing concern around surveillance and its impact on learner privacy, the importance of the de-identification of data before it is made available for institutional use becomes crucial. The need to shape student behavior while ensuring their anonymity within the larger data set is important.
- Ownership of data: The question of the ownership of data is equally important. How can administrators ensure that the data is not being used for unrelated purposes? Moreover, who has the right to determine how this data is used? Students must hold at least some degree of control over how their data is used. Additionally, how long the LMS keeps the data before it’s deleted is another pertinent question
Ensuring Morality in learning analytics
Despite the uncertainties, the rapid growth of learning analytics means that educators must consider not just the vast opportunities it offers for effective decision making in education, but also the ethical challenges in institutionalizing it to drive student support. Any educational institution that collects student data for learning analytics must address the issues outlined above. This may have implications on time and cost spent to ensure ethical use of data, but considering the benefits, it remains an entirely necessary exercise.
While there remains much discussion in the e-Learning industry about the ethical obligations of educational providers, they can start with establishing a basic code of conduct that protects them and their learners from information abuse.
The inherent opportunities and perils of having access to and analyzing big data calls for a careful consideration of its ethical dimensions and challenges. Educational providers must then develop a framework to overcome these issues while extracting maximum information from the analytical data to increase the quality & effectiveness of teaching & learning.