Evidence of the month: February 2015
The field of learning analytics is working to define frameworks that can structure the legal and ethical issues that scholars and practitioners have to take into account when planning and applying analytics to their learning contexts. However, the main focus of this work to date has been higher education. This paper reflects on the need to extend these ethical frameworks to cover other approaches to learning analytics; specifically, small-scale classroom-oriented approaches designed to support teachers in their practice.
This reflection is based on three studies in which a teacher-led learning analytics approach was employed in higher education and primary school contexts. The paper describes the ethical issues that emerged in these learning scenarios, and discusses them with regard to: the overall learning analytics approach, the particular solution to learning analytics adopted, and the educational contexts where the analytics were applied.
This work is presented as a first step towards the wider objective of providing a more comprehensive and adapted ethical framework to learning analytics that is able to address the needs of different learning analytics approaches and educational contexts.
The paper defines privacy as the regulation of how personal digital information is being observed by the self or distributed to other observers. It defines ethics as the systematization of correct and incorrect behaviour in virtual spaces according to all stakeholders.
Principles identified within this paper are:
- student control over data
- rights of access
- accountability and assessment
The authors argue that, by discussing the various aspects within each of these categories, institutions have mechanisms to assess their initiatives and achieve compliance with current laws and regulations as well as with socially derived requirements.
In terms of the Evidence Hub proposition 'Learning analytics are used in an ethical way', the need for a paper such as this to be written implies that appropriate measures are being developed but are not yet in place. As the authors note, 'The ethical and privacy issues derived from these scenarios are not properly addressed.'
Summary provided within the paper:
What is already known about this topic
• Learning analytics offers the possibility of collecting detailed information about how students learn.
• The ethical and privacy issues derived from these scenarios are not properly addressed.
• There is a need to clarify how these issues must be addressed from the early stages of the deployment of a learning analytics scenario.
What this paper adds
• An account of how the main legislations are advancing in the general area of privacy.
• A comparison of how other disciplines such as medicine have dealt with privacy issues when collecting private information.
• The description of a group of practical principles in which to include all the ethical and privacy-related issues present when deploying a learning analytics application.
Implications for practice and/or policy
• Designers may now take into account these principles to guide the implementation of learning analytics platforms.
• Students are now aware of the different categories of issues that must be addressed by applications collecting data while they learn.
• Instructors now have a more detailed account of the issues to address when adopting learning analytics techniques.
There is growing concern about the extent to which individuals are tracked while online. Within higher education, understanding of issues surrounding student attitudes to privacy is influenced not only by the apparent ease with which members of the public seem to share the detail of their lives, but also by the traditionally paternalistic institutional culture of universities.
This paper explores issues around consent and opting in or out of data tracking. It considers how three providers of massive open online courses (Coursera, EdX and FutureLearn) inform users about data usage. It also discusses how higher education institutions can work toward an approach that engages students and informs them in more detail about the implications of learning analytics on their personal data.
The paper restates the need to
- develop a coherent approach to consent, taking into account the findings of research into how people make decisions about personal data
- recognise that people can only engage selectively in privacy self management
- adjust the timing of privacy law to take into account that data may be combined and reanalysed in the future
- develop more substantive privacy rules
This paper was nominated for the best paper award at the 2015 Learning Analytics and Knowledge conference.
This paper reports on the research findings of the Open Academic Analytics Initiative, and features eight findings.
Most positive is the finding that relatively simple intervention strategies designed to alert students early in a course that they may be at risk academically can have a positive impact on student learning outcomes such as overall course grades.
Most concerning is the finding that interventions can have unintended consequences, such as triggering students to withdraw from courses to avoid academic and financial penalties.
- The feasibility of implementing an open‐source early‐alert prototype for higher education, and provides a detailed account of the challenges and design criteria used in implementing such a system.
- The strength of scores derived from partial contributions to the student’s final grade as predictors of academic performance.
- How these predictive models can help the instructor detect students at academic risk earlier in the semester.
- Initial evidence that predictive models can be imported from the academic context in which they were developed to different academic contexts while retaining most of their predictive power.
- That there may be benefits associated with customizing imported predictive models using local institutional data as a means to enhance their predictive power further.
- That there are no apparent gains between providing students with an online academic support environment and simply making students aware of their potential academic risk.
Approaches taken to understand the opportunities and ethical challenges of learning analytics necessarily depend on a range of ideological assumptions and epistemologies. This paper proposes a socio-critical perspective on the use of learning analytics. Such an approach highlights the role of power, the impact of surveillance, the need for transparency and an acknowledgment that student identity is a transient, temporal and context-bound construct. Each of these affects the scope and definition of the ethical use of learning analytics. The authors propose six principles as a framework for a number of considerations to guide higher education institutions to address ethical issues in learning analytics and challenges in context-dependent and appropriate ways.
: Slade, Sharon, & Prinsloo, Paul. (2013). Learning analytics: ethical issues and dilemmas. American Behavioral Scientist.
Institutional policy frameworks should provide not only an enabling environment for the optimal and ethical harvesting and use of data, but also clarify: who benefits and under what conditions, establish conditions for consent and the deidentification of data, and address issues of vulnerability and harm. A directed content analysis of the policy frameworks of two large distance education institutions shows that current policy frameworks do not facilitate the provision of an enabling environment for learning analytics to fulfil its promise.
: Prinsloo, P, & Slade, Sharon. (2013, 8-12 April). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. Paper presented at the Third International Learning Analytics & Knowledge Conference (LAK13), Leuven, Belgium.