Ethics in Predictive Learning Analytics: An Empirical Case Study on Students Perceptions in a Northern Irish University

Ethics in Predictive Learning Analytics: An Empirical Case Study on Students Perceptions in a Northern Irish University

Paul Joseph-Richard (Ulster University, UK) and James Onohuome Uhomoibhi (Ulster University, UK)
DOI: 10.4018/978-1-7998-7103-3.ch004
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Abstract

Most universities collect large amounts of students' data to enhance teaching, understand student behaviour, and predict their success. However, such practices raise privacy and ethical issues due to sensitive data harvesting practices. Despite the recognised importance of this topic, few empirical studies address how students perceive the ethical issues related to predictive learning analytics (PLA). To redress this, interview data collected from 42 undergraduate and postgraduate students in a Northern Irish university were thematically analysed. Findings suggest that there are at least three distinct groups of students having varying assumptions about ethics in PLA. They are (1) naïve and trusting, (2) cautious and compromising, and (3) enlightened and demanding, and all of them tend to narrowly focus only on the issue of informed consent. An empirically supported argument for the need for PLA researchers to recognise the within-group variations in student populations and to educate all types of students in issues related to ethics is presented.
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Introduction

Higher education institutions use a variety of computation‐based technologies to gather and analyse data while students interact in a learning environment, for activities such as completion of online tasks, accessing learning materials, submitting assignments and create postings in discussion forums. As students leave their digital footprints, several issues emerge in relation to the rights and responsibilities stakeholders have in relation to students’ data, the ways of obtaining an informed consent from students, and whether students can opt out of institutional data collection practices, to name a few. There is an increasing scholarly and practitioner interest in understanding more about students’ awareness, consent, data ownership and control, the obligation to act, the kinds of institutional interventions, and the impacts on student behaviour. This body of literature forms the field of ethics in learning analytics and it is steadily growing. Learning Analytics (LA), is often defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (Siemens, 2011). LA is used to understand students’ learning needs, build better pedagogies, target at-risk students, assess factors affecting student successes, allocate resources and inform institutional strategies on student retention and progression (Rienties et al. 2016). Within this broader field of learning analytics, descriptive learning analytics presents what happened in a learning context, diagnostic learning analytics explains why something has happened, predictive learning analytics (PLA) forecasts what might happen and prescriptive learning analytics instructs how could institutions make something happen (Davis, 2013). Scholars tend to agree that students’ engagement in LA in general, and in PLA discourses in particular, has been neglected in literature (West et al. 2020), and therefore, the ethical issues triggered by students’ disengagement in PLA is the focus of this chapter. We present an empirical case study that seeks to explore students’ perceptions on their personal data is being utilised by the University for the purposes of making predictions about their academic success.

This study contributes to the PLA literature at least three ways: first, we empirically establish that students have a relatively a narrower view of ethics in LA than what is presented in the literature and they focus only on a limited set of issues, related to informed consent. Second, it highlights the importance of including students as key stakeholders in the conversations about ethics in PLA. Third, it emphasises the need to recognise the within-group variations in student populations, and to educate all types of students in issues related to ethics so that students as a collective, develop a more holistic understanding of this complex issue of ethics in PLA.

Key Terms in this Chapter

Ethics: In general, it refers to a philosophy of morality that involves systematising, defending, and recommending concepts of right and wrong conduct; it is a fluctuating moral code of norms and conventions that exist in society externally to a person. In the context of LA, it refers to the systemisation of correct and incorrect behaviour in virtual spaces according to all stakeholders.

Privacy: Generally, privacy refers to a living concept made out of continuous personal boundary negotiations with the surrounding ethical environment; it is an intrinsic part of a person's identity and integrity. In the context of LA, privacy is defined as the regulation of how personal digital information is being observed by the self or distributed to other observers.

Contextual Integrity: Nissenbaum (1998) developed the concept of privacy as contextual integrity to propose a normative framework that evaluates the flow of information about individuals. It assumes that our privacy is associated with and regulated by the flow of information based on norms that are context-relative. These norms include context, actors, attributes, and transmission principles and they affect the flow of information from information senders to information receivers to information subjects.

Prescriptive Learning Analytics: A branch of learning analytics that aims to instruct how could institutions make something happen in a learning context.

Predictive Learning Analytics: A branch of learning analytics that aims to forecast what might happen in a learning context.

Descriptive Learning Analytics: A branch of learning analytics that aims to present what happens/ed in a learning context.

Diagnostic Learning Analytics: A branch of learning analytics that aims to explore why and how something has happened in a learning context.

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