Customize Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

Categories
Publications

A Data Protection Framework for Learning Analytics

Since becoming involved in Jisc’s work on learning analytics, I’ve been trying to work out the best place to fit the use of students’ digital data to improve education into data protection law. I’ve now written up those thoughts as a paper, and submitted it to the Journal of Learning Analytics. As the abstract says:

Most studies on the use of digital student data adopt an ethical framework derived from human-studies research, based on the informed consent of the experimental subject. However consent gives universities little guidance on the use of learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses involve an unacceptable risk of harm. Obtaining consent when students join a course will not give them meaningful control over their personal data three or more years later. Relying on consent may exclude those most likely to benefit from early interventions.

This paper proposes an alternative framework based on European Data Protection law. Separating the processes of analysis (pattern-finding) and intervention (pattern-matching) gives students and staff continuing protection from inadvertent harm during data analysis; students have a fully informed choice whether or not to accept individual interventions; organisations obtain clear guidance: how to conduct analysis, which analyses should not proceed, and when and how interventions should be offered. The framework provides formal support for practices that are already being adopted and helps with several open questions in learning analytics, including its application to small groups and alumni, automated processing and privacy-sensitive data.

As a current student and (twice) alumnus, I prefer to think that my own data will be handled according to these ideas, rather than on the basis that I gave informed consent mumble years ago.

The paper was published as Cormack, A. N. (2016). A Data Protection Framework for Learning Analytics. Journal of Learning Analytics, 3(1), 91–106. https://doi.org/10.18608/jla.2016.31.6

By Andrew Cormack

I'm Chief Regulatory Advisor at Jisc, responsible for keeping an eye out for places where our ideas, services and products might raise regulatory issues. My aim is to fix either the product or service, or the regulation, before there's a painful bump!

Leave a Reply

Your email address will not be published. Required fields are marked *