As the field of learning analytics matures, and discourses surrounding the scope, definition, challenges, and opportunities of learning analytics become more nuanced, there is benefit both in reviewing how far we have come in considering associated ethical issues and in looking ahead. This chapter provides an overview of how our own thinking has developed and maps our journey against broader developments in the field. Against a backdrop of technological advances and increasing concerns around pervasive surveillance and the role and unintended consequences of algorithms, the development of research in learning analytics as an ethical and moral practice provides a rich picture of fears and realities. More importantly, we begin to see ethics and privacy as crucial enablers within learning analytics. The chapter briefly locates ethics in learning analytics in the broader context of the forces shaping higher education and the roles of data and evidence before tracking our personal research journey, highlighting current work in the field, and concluding by mapping future issues for consideration.

Ethics and Learning Analytics: Charting the (Un)Charted

As learning analytics activity has increased, a variety of ethical implications and considerations have emerged, though a significant research gap remains in explicitly investigating the views of key stakeholders, such as academic staff. This paper draws on ethics-related findings from an Australian study featuring two surveys, one of institutional leaders (n = 22) and one of academic staff (n = 353), as well as a set of follow-up interviews (n = 23) with academic level staff. A self-selecting sample of participants was asked about the ethical issues they see as important in learning analytics and about the types of ethical principles or considerations they thought should guide learning analytics use. Data showed participants’ views did tend to align with established ethical principles, though the language used to express this varied widely. Building on, and in response to, both the data and the literature review the paper proposes an ethical decision making framework that encourages institutional leaders and those involved in implementing learning analytics to consistently apply and document ethical decision making processes as part of an overall approach to developing well-aligned and transparent institutional policies and broader ethical literacy.

Putting an ethical lens on learning analytics

With﻿ the﻿ rapid﻿ growth﻿ of﻿ online﻿ education﻿ in﻿ recent﻿ years,﻿ Learning﻿ Analytics﻿ (LA)﻿ has﻿ gained﻿ increasing﻿attention﻿from﻿researchers﻿and﻿educational﻿institutions﻿as﻿an﻿area﻿which﻿can﻿improve﻿the﻿ overall﻿effectiveness﻿of﻿ learning﻿experiences.﻿However,﻿ the﻿lack﻿of﻿guidelines﻿on﻿what﻿should﻿be﻿ taken﻿into﻿consideration﻿during﻿application﻿of﻿LA﻿hinders﻿its﻿full﻿adoption.﻿Therefore,﻿this﻿article﻿ investigates﻿the﻿issues﻿that﻿should﻿be﻿considered﻿when﻿approaching﻿the﻿design﻿of﻿LA﻿experiences﻿ from﻿the﻿data﻿preparation﻿perspective.﻿The﻿obtained﻿results﻿highlight﻿a﻿validated﻿LA﻿framework﻿of﻿ twenty-two﻿designing﻿issues﻿that﻿should﻿be﻿considered﻿by﻿various﻿stakeholders﻿in﻿different﻿contexts﻿ as﻿well﻿as﻿a﻿set﻿of﻿guidelines﻿which﻿can﻿enhance﻿designing﻿LA﻿experiences. KEywoRDS Analysis, Adaptive Learning, E-Learning Assessment, Effectiveness, Learning Analytics, Smart Learning Environment

A Complete Validated Learning Analytics Framework: Designing Issues from Data Preparation Perspective

This paper discusses the benefits and challenges associated with supporting learner trust and engagement in personalized learning. Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Quality of personalization depends on information collected from the learner and stored in a personal profile. The authors elaborate on research and design solutions to support privacy, trust and engagement in the context of the Learning and Performance Support (LPSS) program at the National Research Council of Canada. The paper builds on earlier work by the authors related to learning personalization, trust and privacy, and on the results of user surveys and usability studies of the LPSS system related to trust, privacy and user engagement.

Supporting Trust and Engagement in Personalized Learning

This single-site case study will seek to answer the following question: how is the concept of privacy addressed in relation to a student success information system within a small, public institution of higher education? Three themes were found within the inductive coding process, which used interviews, documentation, and videos as data resources. Overall, the case study shows an institution in the early stages of implementing a commercial learning analytics system and provides suggestions for how it can be more proactive in implementing privacy considerations in developing policies and procedures.

Student Privacy and Learning Analytics

Learning Analytics is an area of practice that impacts the legal and ethical obligations of educational institutions. New legislative regimes, growing concern about online privacy, and the affordances of the data being collected mean Learning Analytics could represent a risk to universities to the same extent that it represents an opportunity. These risks augur the need for institutions to develop formal practice and/or policy frameworks around Learning Analytics to define supported practice, actively manage risks and begin to build trust and ethical practice through transparency. There is a danger for Australian universities that the development of such “checks and balances” are not keeping pace with the technological advancements in this field. This paper outlines how one university is seeking to provide a frame for lawful and ethical practice of Learning Analytics through a Code of Practice.

Clearing the Fog: A Learning Analytics Code of Practice

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Clearing the Fog: A Learning Analytics Code of Practice