The increasing pervasiveness of data and analytics in higher education creates many new opportunities to support and enhance learning environments for students. As a result, there is growing interest in the use of student data for an ever-expanding range of possible improvements to teaching and learning practices. However, a major issue that is often neglected in the implementation of analytics-based initiatives is the ethics of how and why data is collected and used. We are in a post-Snowden era, and recent events such as the highly publicized mining of Facebook data to influence political advertising by Cambridge Analytica, and the growing awareness of the uses and abuses of analytics and AI for decision-making, have resulted in increasing concern among the general public about the potential misuse of data. In educational contexts, similar cases are starting to emerge in which learning system vendors are using data to conduct educational experiments without the knowledge of students, parents, or teachers. For example, Pearson Education’s randomized control group studies of motivational messages to 9,000 college and university students drew criticism when student data was used without their knowledge or consent. Such events call attention to the importance of having well-considered policies and guidelines to ensure that learning analytics can be used in an ethical way within educational institutions.
The ethical considerations behind the use of student data and the design of analytics systems that will use this data are complex, and, to date, few institutional approaches have addressed ethical issues in all their complexity. Although there are some promising examples of work undertaken by a number of institutions internationally, most of these efforts have emphasized high-level, one-size-fits-all approaches to ethical policies and frameworks and have been primarily driven by privacy concerns. In such a complex context a more nuanced approach to the ethics of data and analytics is needed, one that accounts for the multiple levels and uses of analytics from design processes through to implementation. While the ethical use of student data for research purposes is well covered in established institutional research ethics guidelines, protocols, and processes, the ethical implications of the use of data and analytics in the day-to-day practices of educational institutions are less clear. It is this focus on how learning analytics can be ethically operationalized within the teaching and learning environment that is the concern of this discussion paper.
The goal of this discussion paper is to explore the key issues relating to the ethical use of learning analytics in Australian higher education institutions. It is authored by an expert group of learning analytics researchers and practitioners and seeks to provide an overview of the current progress that has been made in relation to ethical practice in the field, as well as to highlight some of the issues that still require further consideration. The discussion paper builds on the outcomes of several national projects that profiled the implementation of learning analytics in Australian higher education institutions and identified ethics as a key issue to be addressed in order to achieve continued growth (e.g. West et al., 2016a; Colvin et al., 2016). It also draws on work already undertaken in the field on the development of ethical frameworks for learning analytics. This discussion paper aims to provide useful information to a range of stakeholders in the higher education sector, recognizing that while there is some commonality, ethical issues may also differ for different internal and external stakeholders. For students and student union groups, it will provide an overview of the different ways that data can be used for learning analytics and the ethical considerations that can inform their provision of consent for such practices.
For teachers, it will raise awareness of the considerations that teachers need to make in using analytics to support their practice. For learning analytics professionals it will inform processes for design, selection, and implementation of learning analytics tools and approaches to ensure ethical practice. For senior management (e.g. DVCAs) and associated data governance groups, it will provide guidance on the issues to be considered at a strategic and operational level to inform the development of institutional policies, processes, and guidelines. Despite the need to keep these different audiences in mind, we also hope to create a common language to facilitate conversations about ethics and analytics among various stakeholders so as to progress approaches to ensuring the ethical use of learning analytics in Australian higher education institutions.
The paper begins with an exploration of the field of learning analytics and examines some of the recent scholarly work on ethical issues to provide context for the discussion. This is followed by an outline of the key ethical principles that are important in the consideration of how learning analytics are designed and used in practice. A review of existing learning analytics ethical frameworks is provided along with examples of real applications of learning analytics in educational environments. This is followed by a range of case studies that illustrate how different institutions, both nationally and internationally, have been working towards ensuring ethical practice in relation to learning analytics. The paper concludes with suggestions for educational leaders and practitioners about what needs to be considered and done to ensure that the ethical practice of learning analytics is acknowledged and operationalized within educational institutions.
As outlined above, the goal of this paper has been to outline and expose key ethical issues associated with the use of learning analytics and data in digital learning environments. The paper has provided a brief introduction to learning analytics and general ethical considerations before proposing eight ethical principles that emerged from the literature in the area. It has reviewed key learning analytics ethical frameworks that already exist and has discussed in detail five common uses of learning analytics for which institutions are increasingly likely to need to have an ethical response. Finally, it has reviewed a number of institutional practices that are setting the benchmark in the ethical use of learning analytics.
The goal of this paper is not to provide a set of firm recommendations for individuals, institutions, or the higher education sector. We feel that this is not our place and, moreover, this is difficult to do in an emerging area of research and practice where tailored, localized responses are more likely to be appropriate. However, we do feel it would be useful to conclude the paper with a number of key considerations that have emerged for us in preparing this paper. These considerations are proposed tentatively below, but are also put forward as something of a “call to arms”. They are intentionally presented and phrased in a way that might provoke educational leaders’ and practitioners’ to think about what they might need to do when it comes to the ethical use of learning analytics within their institutions.
We need to:
● Recognize that the ethics of learning analytics is very complex. While seemingly obvious, it is important to acknowledge the diverse ethical principles that are at play – we have proposed eight – and be mindful of the ways in which administrative, educational, and learning analytics platforms and systems generate data that intersect with each of these principles. There is a need to recognize how data can and are already being used and start interrogating these uses more closely with a structured ethical lens.
● Develop clear principles and guidelines on data use in learning and teaching. When one considers what is the standard operating procedure for the use of data in research, it is clear how much work needs to be completed in many institutions before they might claim to have similarly robust principles and procedures for the ethical use of data in the area of learning and teaching. It is clear from our preparation of this paper that these principles and guidelines need to be established and need to sit outside the technology systems and applications that are used by institutions and individuals. In short, the technological systems employed should not set the principles of what data is collected and used within the institution.
● Actively engage with multiple stakeholders. There needs to be a “community-based” conversation about the ethical use of learning analytics and it is critically important to include multiple stakeholders, particularly students and staff, in this conversation. Such conversations will or should provide the dual purpose of sharing knowledge and information about the ethics of learning analytics while also promoting a shared communal understanding of the relevant issues and what should be developed and enacted. Such conversations could also consider how the university community can engage in advocacy about the design of educational technology and learning analytics systems that are being developed internally and those developed commercially external to the university.
● Establish transparency and trust. Developing clear principles and genuinely engaging with stakeholders will go some way towards establishing transparency and trust across institutions in the area of learning analytics. The reality is that compared to more conventional technologies, advanced technologies that rely on AI or big data are often complex and difficult for non-technical people to understand. As we increasingly use advanced technologies that we do not understand all the time in our daily lives, system opacity might not appear to be a problem per se. However, we entrust our lives to aerospace engineers, surgeons, and financial advisors knowing that they are regulated professionally, and we have experienced first-hand the benefits. In contrast, advanced educational technology systems that are difficult to understand are a much newer phenomenon: there has yet to be a generation of children or university students who have been immersed in such environments through to graduation. This highlights how important it is that stakeholders (e.g. educators, students, parents, instructional designers, policymakers) can trust the tool, and moreover, that educational institutions have suitably qualified staff asking probing questions of learning analytics and adaptive learning systems and providers. There is a need to establish or ensure transparency in whatever we do in this area. Advanced educational technologies that employ complex learning analytics and AI should not be regarded as “Black Box” tools to be trusted but should be transparent and able to be challenged.
● Avoid reinventing the wheel. What is clear from the reviews undertaken in the preparation of this paper is that while discussion, research, and practice in the area of the ethics of learning analytics is relatively new, there are already well-established models, systems, principles, and frameworks to draw on. We have highlighted a couple, but there are more. Institutions can, therefore, build on this foundation when considering their ethical responses to the use of learning analytics. It is also worth considering whether, in addition to individual institutional responses to the issues raised in this paper, there is value in a sector-wide response or manifesto.
● Get a move on. Higher education institutions, leaders, and educational technology experts are in danger of being accused of burying their heads in the sand when it comes to the ethics of learning analytics. Experienced practitioners and researchers will be well aware that the issue of ethics in the field of learning analytics has been “burning” for some time. The Cambridge Analytica scandal (among others), the regular news reports of data breaches, and the GDPR are yet more reminders that, aside from everything else, the ethical use of students’ and other data is a legal issue for universities. There is a need for action in order to prevent this area from becoming a significant legal risk for institutions.
● Develop processes to revisit and recast practice. While there is clearly value in having well-crafted principles, policies, and guidelines within an institution, it is also clear that as technology systems and approaches to data analytics evolve and change, new approaches to practice may need to be developed. It is essential that institutions do not adopt a “set and forget” mentality to the ethics of learning analytics as there will inevitably be a need to review and update policy and practice in this fast-moving area.
These seven considerations are not comprehensive, but we present them to trigger conversations about what action can be taken and what can be done in institutions and across the sector. Fundamentally, many of the questions and issues both institutions and individuals face in the ethical use of learning analytics are about data governance, management, and use. While not intending to simplify the complexity of this challenge, this may be reduced to some key questions:
Who has access?
To what data?
To do what?
For what reason?
And what has been learned from this?
We hope this discussion paper provides a useful contribution to the ongoing conversation about a critical issue in higher education in Australia.
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FULL Paper PDF file:The Ethics of Learning Analytics in Australian Higher Education
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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.