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Despite evidence that learning analytics has become institutionalised within higher education since its emergence in 2011, there remain questions regarding its impact on informing curricula, pedagogy and ultimately, on student success. A variety of factors can impact on the successful implementation of learning analytics. Despite its huge potential, learning analytics may become stuck in the middle of, inter alia, the need to balance operational needs and resource allocation, and different perceptions of learning, agency and loci of control in learning, teaching and macro-societal factors. In this conceptual paper, we propose an institutional cartography of learning analytics and explore the impact of a number of micro, meso and macro institutional factors that may impact and shape the institutionalisation of learning analytics. We utilise a socio-critical model for understanding student success as a conceptual basis for developing this cartography.

Learning analytics is an emerging but rapidly growing field seen as offering unquestionable benefit to higher education institutions and students alike. Indeed, given its huge potential to transform the student experience, it could be argued that higher education has a duty to use learning analytics. In the flurry of excitement and eagerness to develop ever slicker predictive systems, few pause to consider whether the increasing use of student data also leads to increasing concerns. This chapter argues that the issue is not whether higher education should use student data, but under which conditions, for what purpose, for whose benefit, and in ways in which students may be actively involved. The authors explore issues including the constructs of general data and student data, and the scope for student responsibility in the collection, analysis and use of their data. An example of student engagement in practice reviews the policy created by the Open University in 2014. The chapter concludes with an exploration of general principles for a new deal on student data in learning analytics.

Optimizing the harvesting and analysis of student data promises to clear the fog surrounding key drivers of student success and retention, and our understanding of the impact of interventions to improve student success. While recognising its potential, it is crucial to deconstruct some of the assumptions regarding learning analytics in the nexus between student data and institutional accountability as well as broader concerns regarding the scope and impact of acts of surveillance. This chapter will explore and contest assumptions regarding the potential of big data in higher education – for example, that big data will, in itself, result in more accuracy or objectivity, that surveillance is uni-directional and that available data provides holistic pictures of student learning. Such assumptions not only impact directly on our view of institutional accountability towards a range of stakeholders, but also on the ethical implications of the harvesting of student data, its use and storage. Assumptions and understanding of the issues surrounding student privacy and institutional accountability are sedimented in institutional policies, operational frameworks based on a technocratic predictive logic inherent in neoliberal and governmentality discourses. Policies and frameworks in higher education institutions should provide enabling and ethical environments for the optimal use of learning analytics as negotiated and student centred praxis. This chapter explores student data privacy in the context of the dominant technocratic predictive logic in learning analytics. We propose a broader framework for the interrogation of learning analytics as an ethical and negotiated contract between institutions and students within an ethics of care.

The growth in using ICT to plan more effective learning in higher education necessitates a critical appropriation of the impact of ‘transactional immediacy’ on students’ experiences of online learning and teaching. As institutions attempt to counter the effect of ‘transactional distance’ by exploring opportunities offered by asynchronous and synchronous online learning environments, the ‘immediacy’ of online education is often valorised. ‘Transactional immediacy’ in online learning can result in experiences of disorientation and dissonance. These experiences, though, are not necessarily negative. This paper shares findings from a study of students’ online journals in the Professional Certificate in Management, offered by the OU Business School. The study found ample evidence of a variety of dislocations which students experience, but also a variety of locating strategies employed by students to deal with the multifaceted disequilibrium some experience in learning online. This dislocation can be technological, epistemological and ontological. In their learning journals, students reflect on such experiences, but also document a variety of strategies to self-author. This paper aims to provide a theoretical foundation for the notion of (dis)location in online learning; presents qualitative research findings regarding the (dis)locating experiences of students, and shares ideas for the design of online learning experiences within a hermeneutical framework of (dis)location. The findings have implications for the design, management and administration of online learning, and specifically for the use of online learning journals.

As higher education becomes increasingly borderless, with technological innovations and globalisation allowing higher education institutions to reach beyond their own geopolitical boundaries, different stakeholder groups have a range of expectations regarding the meaning of an ‘international’ curriculum. These expectations may differ starkly from the hype and rhetoric of the discourses of the internationalisation of higher education. Often these expectations and assumptions result in specific claims regarding curriculum design, development and delivery. Students are registering to study with overseas institutions which offer programmes often developed within considerably different cultures and contexts than the students’ own. Institutions offering ‘international’ qualifications are in danger of (and often accused of) prescribing Anglophone epistemologies as universally true and applicable. Such curricula may result in cultures losing their distinctive features or becoming syncretistic mixtures of cultural practices dislocated from their original locations. This paper shares the findings of a research project in the context of a large distance learning Business School in which 500 students, faculty, tutors and employers were surveyed regarding their expectations of an MBA with a stated claim of providing students with an ‘international’ learning experience. The findings reveal that the different stakeholder groups have very different views of what they expect from both an international business curriculum and an international mix of students. While all stakeholders stated several advantages of being exposed to diverse contexts (local and global, private and public) and different cultures; students and sponsors considered the ‘international’ nature of the curriculum to be less important than tutors and faculty. The findings will be of interest to faculty, curriculum and instructional designers, tutors, employers and higher education management.

Recently a critical turn has emerged in the open education literature. In addition to voices which have critiqued open education as under-theorised (Bayne, Knox & Ross, 2015; Edwards, 2015; Gourlay, 2015; Knox, 2013; Oliver, 2015; Veletsianos & Kimmons, 2012), another strand has called upon scholars and practitioners to refocus their attention on open educational practices (OEP). From this perspective, open forms of education (like education in general) are framed as a range of processes undertaken by human beings, rather than consisting specifically in the resources they create or amend. This framing appears to offer the possibility of deeper insights into actual practices, but whereas the forms of openness afforded by OER or MOOCs are well understood, it is less obvious what exactly makes a practice open. However, this inherent ambiguity might be viewed as a strength rather than a weakness of OEP: while much of the literature discussing OEP has framed the concept in relation to OER, there is a more expansive concept of OEP which recognises other forms of openness and ways of opening (Havemann, 2016; Cronin, 2017; Cronin & MacLaren, 2018; Roberts et al, 2018). Taking its cue from this line of enquiry, this chapter considers the question of what is open about (or opened by) open education, and how the concept of OEP can potentially aid this investigation. It focuses on a case study of practice at Birkbeck, University of London, which is an institution of higher education that itself grew out of a particular instance of opening almost 200 years ago. The case study will illustrate the senses in which Birkbeck, along with the particular case of practice under study, is characterised by an interplay of openings and closures.

This paper outlines some of the material assemblages that are formed in international distance education (DE) in Africa. It offers a first exploratory study of materialities in DE and how they potentially distribute and aggregate to form a network to provide education. Through the use of interviews, students lived experiences are explored to unpack the multiplicity of networks needed to overcome the de‐aggregated and distributed institution. The multiplicity of networks that form in DE brings challenges that question how spaces become connected and disconnected and how different materialities shape DE. The materialities in DE produce forces and effects, such as translocal and transmobilites that are more than just the human actor, but extrude materials, networks, and connectives that transform continuously. The interconnectivities of the university and home or institution and students are brought together through enabling technology, but infrastructure does not always have the ability for the facilitation of aggregation.

Few would contest the impact of technology on modern day society. There are, however, wide-ranging opinions and contestations regarding the social and ethical implications of the increasing entanglement of our lives with technology (Introna, 2017; Marx, 2016; Robertson & Travaglia, 2017). Introna (2017) suggests that “At the center of this technology/society interrelationship we find many complex questions about the nature of the human, the technical, agency, autonomy, freedom and much more” (para. 1). Central to our entanglement in this techno-societal complex is the issue of the use of personal data and the scope and limitations of individuals’ agency (a) to make rational, informed choices regarding consent to having their data collected, analyzed, and used (Prinsloo & Slade, 2015); (b) for freely gifting (Kitchin, 2013, p. 263) unrequested data in ways that suggest digital promiscuity (Payne, 2014); and (c) to negotiate terms and conditions around receiving benefits in exchange for personal data, in a phenomenon known as the “privacy calculus” (Knijnenburg, Raybourn, Cherry, Wilkinson, Sivakumar, & Sloan, 2017, para. 1). It is also important to note the increasing automated and directed surveillance of digital users without their knowledge or consent (Kitchin, 2013), which raises “unprecedented challenges to how we currently elicit, secure, and sustain user consent” (Luger, Rodden, Jirotka, & Edwards, 2014, p. 613). The Big Data revolution (Kitchin, 2014) with its accompanying generative mechanisms for extracting data “has become an idea with social and political power in its own right” (Robertson & Travaglia, 2017, para. 1). The reductive quantification of complex social phenomena and the combination of different datasets suggest the need to (re)consider the notion of consent, the scope and limitations of informed consent, in general (Wilson, 2017), and, more specifically, consent in the context of higher education. As the volume, velocity, and variety in data h

In this chapter, we examine the notion of responsible learning analytics. Such an approach requires institutions to take an answerable and response-able approach to using student data. Learning analytics, like all educational technology, is shaped by a range of social, political, economic and cultural agendas. As such, this chapter argues that ethical and responsible learning analytics is central to a consideration of each of the eight dimensions of Khan’s e-learning framework (Khan, 2010). The chapter concludes by suggesting a number of pointers for higher education institutions to ensure the adoption of a responsible and responsive approach to learning analytics.

Much of the research on how social media is embedded into the educational practices of higher education students has a Western orientation. In concentrating on a case study of the varied ways in which African International Distance Education (IDE) students actively use social media to shape their learning experiences, we discuss an under-researched group. The paper draws on analysis of 1295 online questionnaires and 165 in-depth interviews with IDE students at UNISA, South Africa, one of the largest providers of IDE globally. WhatsApp emerges as ‘the’ key social media tool that opens up opportunities for IDE students to transfer, translate and transform their educational journey when studying ‘at a distance’. Although WhatsApp does provide a ‘space of opportunity’ for some students, this is framed through socio-technical marginalisation, itself a reflection of demographic legacies of inequality. Exploring social media practices though the case of African IDE students places these students centre stage and adds to the awareness of the multiple centres from which international education is practiced.

While many strategies for protecting personal privacy rely on regulatory frameworks, consent, and anonymizing data, they are not always effective. Terms and Conditions often lag behind advances in technology, software, and user behaviours, and consent to use data for a range of unclear purposes may be provided unwittingly. As the commercial market for (student) data expands, so does the number of brokers who move, share and sell data across continents and legislative environments. This paper reviews four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how consent to collect and use data is described to potential users, and how that consent applies at micro, meso, and macro levels. This paper proposes a need for greater transparency around the implications of users granting consent at the point of registration. Further, it highlights that though MOOC providers have a responsibility to make clear the potential uses and sharing of user data, users themselves should also be more aware and consider how meaningful student agency can be achieved.

As higher education increasingly moves to online and digital learning spaces, we have access not only to greater volumes of student data, but also to increasingly fine-grained and nuanced data. A significant body of research and existing practice are used to convince key stakeholders within higher education of the potential of the collection, analysis and use of student data to positively impact on student experiences in these environments. Much of the recent focus in learning analytics is around predictive modeling and uses of artificial intelligence to both identify learners at risk, and to personalize interventions to increase the chance of success. In this paper we explore the moral and legal basis for the obligation to act on our analyses of student data. The obligation to act entails not only the protection of student privacy and the ethical collection, analysis and use of student data, but also, the effective allocation of resources to ensure appropriate and effective interventions to increase effective teaching and learning. The obligation to act is, however tempered by a number of factors, including inter and intra-departmental operational fragmentation and the constraints imposed by changing funding regimes. Increasingly higher education institutions allocate resources in areas that promise the greatest return. Choosing (not) to respond to the needs of specific student populations then raises questions regarding the scope and nature of the moral and legal obligation to act. There is also evidence that students who are at risk of failing often do not respond to institutional interventions to assist them. In this paper we build and expand on recent research by, for example, the LACE and EP4LA workshops to conceptually map the obligation to act which flows from both higher education's mandate to ensure effective and appropriate teaching and learning and its fiduciary duty to provide an ethical and enabling environment for students to achieve success. We exami

In light of increasing concerns about surveillance, higher education institutions (HEIs) cannot afford a simple paternalistic approach to student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analyzed, used, and shared. It is clear from literature that basic opting in or out does not sufficiently address many of the complex issues in the nexus of privacy, consent, vulnerability, and agency. The notion of vulnerability (institutional and individual) allows an interesting and useful lens on the collection and use of student data. Though both institutional and individual vulnerability need to be considered, this paper focuses specifically on student vulnerability. In this conceptual article, we explore student vulnerability in the nexus between realizing the potential of learning analytics; the fiduciary duty of HEIs in the context of their asymmetrical information and power relations with students; and the complexities surrounding student agency in learning analytics. This article expands on an earlier framework developed by Prinsloo and Slade (2015). It aims to explore ways to decrease student vulnerability, increase their agency, and empower them as participants in learning analytics — moving them from quantified data objects to qualified and qualifying selves.

As in many fields, most papers in the learning analytics literature report success or, at least, read as if they are reporting success. This is almost certainly not because learning analytics research and activity are always successful. Generally, we report our successes widely, but keep our failures to ourselves. As Bismarck is alleged to have said: it is wise to learn from the mistakes of others. This workshop offers an opportunity for researchers and practitioners to share their failures in a lower-stakes environment, to help them learn from each other’s mistakes.

Amidst vast changes sweeping the higher education landscape, there is an increasing need to use data to increase the effectiveness of teaching and learning, and subsequently, ensure accountability and efficiency in an increasingly resource-constrained and competitive higher education landscape (Altbach et al, 2009). Learning analytics as an emerging discipline and practice promises to contribute to more effective teaching, learning and resource allocation through the collection, analysis and use of student data (Prinsloo & Slade, 2014). As teaching and learning move progressively online and digital, the amount of student data increases exponentially, opening opportunities for data-informed strategies and pedagogies. Though there is no doubt that the collection, analysis and use of student digital data do offer huge potential, there are also a number of risks and ethical challenges such as the belief that data is neutral; the role of algorithms and the algorithmic turn in higher education; the assumptions and epistemologies informing the collection and analysis and use of data; and the increasing possibilities for discriminating against already vulnerable and at-risk students (Slade & Prinsloo, 2013; Prinsloo & Slade, 2014). This paper follows Prinsloo (2014) who proposes that ‘Learning analytics are a structuring device, not neutral, informed by current beliefs about what counts as knowledge and learning, coloured by assumptions about gender/race/class/capital/literacy and in service of and perpetuating existing or new power relations.’ Though the collection, analysis and use of student digital data aims to decrease students’ vulnerability and risks of failing or dropping out, there is also the possibility that in the light of the asymmetrical power relationship between student and institutions of higher learning, students’ vulnerability may actually be exacerbated. As higher education institutions (HEIs) optimise the potential of learning analytics, this paper prop

Though concern about student attrition and failure is not a new phenomenon, higher education institutions (HEIs) have struggled to significantly reduce the revolving door syndrome. Open distance learning higher education is particularly susceptible to high student attrition. Despite a great deal of research into the student journey and factors impacting on likely success, we are not necessarily closer to understanding and being able to mitigate against student attrition. Learning analytics as emerging discipline and practice promises to help penetrate the fog… This case study describes work undertaken at the Open University in the UK to investigate how a learning analytics approach allows the University to provide timely and appropriate student support in a cost-effective manner. It includes a summary of the establishment of curriculum-based student support teams and a framework which defines more standardised student support informed by both student data and an enhanced knowledge of the curriculum. The primary aim of student support teams is to proactively support students through their study journey and to optimise their chances of reaching their declared study goals. Higher education institutions (HEIs) are making increasing use of learning analytics to support delivery of timely and relevant student support. The Open University in the UK, like other HEIs, knows a great deal about its students before they start to study and is able to track student behaviours once study has begun. Until recently, the university has not taken full advantage of the additional insight offered by such information. This paper describes the framework of support interventions established for all student support teams and describes the learning analytics approach used to support that framework.

The Open University (OU) is a large, open distance learning institution with more than 200,000 students. In common with many other higher education institutions (HEIs), the University is looking more closely at its use of learning analytics. Learning analytics has been defined as the collection and analysis of data generated during the learning process in order to improve the quality of learning and teaching (Siemens, Dawson, & Lynch, 2013). In the context of the Open University, learning analytics is the use of raw and analysed student data to, inter alia, proactively identify interventions which aim to support students in completing their study goals. Such interventions may be designed to support students as individuals as well as at a cohort level. The use of a learning analytics approach to inform and provide direction to student support within the Open University is relatively new and, as such, existing policies relating and referring to potential uses of student data have required fresh scrutiny to ensure their continued relevance and completeness (Prinsloo & Slade, 2013). In response, The Open University made the decision to address a range of ethical issues relating to the University’s approach to learning analytics via the implementation of new policy. In order to formulate a clear policy which reflected the University’s mission and key principles, it was considered essential to consult with a wide range of stakeholders, including students.

Higher education, and more specifically, distance education, is in the midst of a rapidly changing environment. Higher education institutions increasingly rely on the harvesting and analyses of student data to inform key strategic decisions across a wide range of issues, including marketing, enrolment, curriculum development, the appointment of staff, and student assessment. In the light of persistent concerns regarding student success and retention in distance education contexts, the harvesting and analysis of student data in particular in the emerging field of learning analytics holds much promise. As such the notion of educational triage needs to be interrogated. Educational triage is defined as balancing between the futility or impact of the intervention juxtaposed with the number of students requiring care, the scope of care required, and the resources available for care/interventions. The central question posed by this article is “how do we make moral decisions when resources are (increasingly) limited?” An attempt is made to address this by discussing the use of data to support decisions regarding student support and examining the concept of educational triage. Despite the increase in examples of institutions implementing a triage based approach to student support, there is a serious lack of supporting conceptual and theoretical development, and, more importantly, to consideration of the moral cost of triage in educational settings. This article provides a conceptual framework to realise the potential of educational triage to responsibly and ethically respond to legitimate concerns about the “revolving door” in distance and online learning and the sustainability of higher education, without compromising ‘openness.’ The conceptual framework does not attempt to provide a detailed map, but rather a compass consisting of principles to consider in using learning analytics to classify students according to their perceived risk of failing and the potential of additi

Despite all the research on student retention and success since the first conceptual mappings of student success e.g. Spady, there have not been equal impacts on the rates of both student success and retention. To realise the potential of learning analytics to impact on student retention and success, mega open distance learning (ODL) institutions face a number of challenges, paradoxes and opportunities. For the purpose of this paper we critique a ‘closed’ view of learning analytics as focusing only on data produced by students’ interactions with institutions of higher learning. Students are not the only actors in their learning journeys and it would seem crucial that learning analytics also includes the impacts of all stakeholders on students’ learning journeys in order to increase the success of students’ learning. As such the notion of ‘Thirdspace’ as used by cultural, postmodern and identity theorists provide a useful heuristic to map the challenges and opportunities, but also the paradoxes of learning analytics and its potential impact on student success and retention. This paper explores some of these challenges, paradoxes and opportunities with reference to two mega ODL institutions namely the Open University in the UK (OU) and the University of South Africa (Unisa). Although these two institutions share a number of characteristics, there are also some major and important differences between them. We explore some of the shared challenges, paradoxes and opportunities learning analytics offer in the context of these two institutions.

Business schools have been accused of being the ‘academy of the apocalypse’, serving and perpetuating greed and ‘rapacious capitalism’. There are increasing calls for business schools to redefine the purpose of business and to empower students to serve broader society whilst being profitable. Social justice is not considered to be high on the agenda of the curricula of many business schools, on the contrary. While the debate rages on regarding the complicity of business schools in pursuing a capitalist path, an important question is whether students and (potential) employers care about social justice as an essential element of the MBA curriculum? This paper shares the findings of a study undertaken to establish the expectations of various stakeholders including students, faculty and employers on the curriculum and pedagogy of the MBA curriculum in an open and distance learning institution. The study indicates that social justice issues are very low on the ‘wish list’ of students and employers, while representatives from the faculty had differing views regarding the value of including social justice issues in the MBA curriculum.

There are many studies regarding the benefit of incorporating learning diaries into learning experiences in higher education, though very little research documenting what students record when those diaries are unstructured. There are several arguments and counter-arguments for providing students with structure in their completion of learning diaries. Structure is said to limit spontaneity, while unstructured learning diaries are considered to be logs or diaries rather than reflective journals. This study presents evidence that unstructured, private learning diaries can assist students to become more self-aware - and aware of others - while learning together online. This research provides useful pointers for managers and designers of online learning experiences regarding the role that online learning diaries can play in facilitating reflection.

This brief chapter explores the feasibility of teaching (a code of) ethics against a background which examines our views around data scientists, data analysis, data and, in particular, student data. It touches upon different approaches to ethics and asks whether teaching ethics would make any difference.

In this paper we explore the potential role, value and utility of a personal code of ethics (COE) for learning analytics practitioners, and in particular we consider whether such a COE might usefully mediate individual actions and choices in relation to a more abstract institutional COE. While several institutional COEs now exist, little attention has been paid to detailing the ethical responsibilities of individual practitioners. To investigate the problems associated with developing and implementing a personal COE, we drafted an LA Practitioner COE based on other professional codes, and invited feedback from a range of learning analytics stakeholders and practitioners: ethicists, students, researchers and technology executives. Three main themes emerged from their reflections: 1. A need to balance real world demands with abstract principles, 2. The limits to individual accountability within the learning analytics space, and 3. The continuing value of debate around an aspirational code of ethics within the field of learning analytics.

"Are students walking around with invisible triage tags attached, that only lecturers can see? Is this fair? Or is it just pragmatic? Like battlefield medical attention, lecturers’ attention is finite. And as class sizes and workloads increase, it is becoming scarcer” (Manning, 2012) It is difficult to understate the scope and impact of the changes facing international and national higher education. Terms such as “disruption” and “innovation”, “disaggregation”, the “unbundling and unmooring”, “revolution”, and “crisis” have become endemic to discourses on the current and future states of higher education. Against this backdrop, higher education institutions increasingly need to make strategic decisions regarding opportunities and alleviating risk. Risk within higher education both mirrors the broader societal dimensions of risk, and also presents additional aspects including the danger of obsolescence, changing funding regimes, the impact of technology on content, assessment and the role of faculty, the increasing diversification of forms of higher education and student populations, and concerns about student success and retention. Within this context, higher education and in particular open distance and elearning (ODeL) increasingly relies on the harvesting, analysis and use of available data to inform strategic decisions regarding enrolment, marketing, curriculum development, the appointment of staff, student assessment and increasingly, strategies that inform initiatives to increase student retention and success. The harvesting and analysis of student data therefore offers opportunities for higher education institutions to respond, timeously and appropriately, to identifying students who are at risk of failing or dropping out. The opportunities offered by learning analytics have, however, also brought to the fore concerns regarding a number of issues such as governmentality, data privacy, consent and other ethical issues and challenges. The central question this

The surprising lack of pressure and speed in the centre of the vortex of a storm are in stark contrast to the force and destruction often experienced at its periphery. Many spectators watching a developing storm will be caught between fear and a desire to escape. The metaphor of a storm has been applied here to the emotions experienced by many students enrolling in online learning courses. Not only do the requirements of studying online collide with personal and professional commitments, the experience of learning online (often in groups) results in many students feeling displaced, scared or out of control. Learning diaries, especially in an online environment, present students with an opportunity to reach the centre of the vortex, though this may not be as quiet and safe as we may have presumed. This paper reports on students’ reflections in their learning diaries as a prescriptive part of the Professional Certificate in Management offered by the Open University. The research focused on the unstructured learning diary entries of 12 students from one tutor group over an 18 day period of a short compulsory online course. This phenomenographic study used grounded theory as methodology to analyse and describe students’ use of their learning diaries. The research found ample evidence that online learning diaries provide students with a safe space to reflect on the vortex around them. Without a quiet and reflective centre, students may be overwhelmed by the wider forces impacting on them. Students’ postings provided rich descriptions of the vortex of studying online and the function of having a centre to which to withdraw. There is, however, also evidence that posting reflections in learning diaries can itself be a dislocating and uncomfortable experience for some learners, while others question its usefulness. The work provides practical and useful information for managers of online learning experiences, instructional designers and curriculum developers

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.

The growth of learning analytics as a means to improve student learning outcomes means that student data is being collected, analyzed, and applied in previously unforeseen ways. As the use of this data continues to shape academic and support interventions, there is increasing need for ethical reflection on operational approvals for learning analytics research. Though there are clear processes for vetting studies resulting in publication of student-gathered data, there is little comparable oversight of internally generated student-focused research. Increasingly, ethical concerns about the collection and harvesting of student data have been raised, but there is no clear indication how to address or oversee these ethical concerns. In addition, staff members who are not typical researchers may be less familiar with approvals processes and the need to demonstrate potential for harm, etc. If current trends point to a range of individuals harvesting and analyzing student data (mostly without students' informed consent or knowledge), how can the real danger of unethical behavior be curbed to mitigate the risk of unintended consequences? A systematic appraisal of the policy frameworks and processes of ethical review at three research institutions (namely, the University of South Africa, the Open University in the United Kingdom, and Indiana University in the United States) provides an opportunity to compare practices, values, and priorities. From this cross-institutional review, a working typology of ethical approaches is suggested within the scope of determining the moral intersection of internal student data usage and application.

Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around the extent to which individuals are routinely and progressively tracked as they engage online. The Internet, the very thing that promised to open up possibilities and to break down communication barriers, now threatens to narrow it again through the panopticon of mass surveillance. Within higher education, our assumptions and understanding of issues surrounding student attitudes to privacy are influenced both by the apparent ease with which the public appear to share the detail of their lives and our paternalistic institutional cultures. As such, it can be easy to allow our enthusiasm for the possibilities offered by learning analytics to outweigh consideration of issues of privacy. This paper explores issues around consent and the seemingly simple choice to allow students to opt-in or opt-out of having their data tracked. We consider how 3 providers of massive open online courses (MOOCs) inform users of how their data is used, and discuss how higher education institutions can work toward an approach which engages and more fully informs students of the implications of learning analytics on their personal data.

The field of learning analytics has the potential to enable higher education institutions to increase their understanding of their students’ learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. There is an inherent assumption linked to learning analytics that knowledge of a learner’s behavior is advantageous for the individual, instructor and educational provider. It seems intuitively obvious that a greater understanding of a student cohort and of the learning designs and interventions to which they best respond would be of benefit to students and, in turn, for the retention and success rate of the institution. Yet, such collection of data and its use faces a number of ethical challenges, including issues of the location and interpretation of data; informed consent, privacy and the de-identification of data; and the classification and management of data. 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. We 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.

Higher education institutions have collected and analysed student data for years, with their focus largely on reporting and management needs. A range of institutional policies exist which broadly set out the purposes for which data will be used and how data will be protected. The growing advent of learning analytics has seen the uses to which student data is put expanding rapidly. Generally though the policies setting out institutional use of student data have not kept pace with this change. 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 de-identification 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.

Diversity, equity and inclusion (DEI) are almost taken-for-granted concepts in the broader context of open education, and specifically in the context of Open Educational Resources (OER) initiatives, projects and practices. GO-GN, a Global OER Graduate Network, set out to investigate and develop its DEI guidelines. This paper reports on the processes and findings of scoping DEI in the context of GO-GN. It also presents some of the project findings and provide the foundations of GO-GN guidelines for DEI. We then suggest some pointers for other OER practices and practitioners in embracing and foregrounding diversity, inclusion and equity.

Much of the research on how social media is embedded into the educational practices of higher education students has a Western orientation. In concentrating on a case study of the varied ways in which African International Distance Education (IDE) students actively use social media to shape their learning experiences, we discuss an under-researched group. The paper draws on analysis of 1295 online questionnaires and 165 in-depth interviews with IDE students at UNISA, South Africa, one of the largest providers of IDE globally. WhatsApp emerges as ‘the’ key social media tool that opens up opportunities for IDE students to transfer, translate and transform their educational journey when studying ‘at a distance’. Although WhatsApp does provide a ‘space of opportunity’ for some students, this is framed through socio-technical marginalisation, itself a reflection of demographic legacies of inequality. Exploring social media practices though the case of African IDE students places these students centre stage and adds to the awareness of the multiple centres from which international education is practiced.

This paper outlines some of the material assemblages that are formed in international distance education (DE) in Africa. It offers a first exploratory study of materialities in DE and how they potentially distribute and aggregate to form a network to provide education. Through the use of interviews, students lived experiences are explored to unpack the multiplicity of networks needed to overcome the de‐aggregated and distributed institution. The multiplicity of networks that form in DE brings challenges that question how spaces become connected and disconnected and how different materialities shape DE. The materialities in DE produce forces and effects, such as translocal and transmobilites that are more than just the human actor, but extrude materials, networks, and connectives that transform continuously. The interconnectivities of the university and home or institution and students are brought together through enabling technology, but infrastructure does not always have the ability for the facilitation of aggregation.

While many strategies for protecting personal privacy rely on regulatory frameworks, consent, and anonymizing data, they are not always effective. Terms and Conditions often lag behind advances in technology, software, and user behaviours, and consent to use data for a range of unclear purposes may be provided unwittingly. As the commercial market for (student) data expands, so does the number of brokers who move, share and sell data across continents and legislative environments. This paper reviews four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how consent to collect and use data is described to potential users, and how that consent applies at micro, meso, and macro levels. This paper proposes a need for greater transparency around the implications of users granting consent at the point of registration. Further, it highlights that though MOOC providers have a responsibility to make clear the potential uses and sharing of user data, users themselves should also be more aware and consider how meaningful student agency can be achieved.

In this paper we explore the potential role, value and utility of a personal code of ethics (COE) for learning analytics practitioners, and in particular we consider whether such a COE might usefully mediate individual actions and choices in relation to a more abstract institutional COE. While several institutional COEs now exist, little attention has been paid to detailing the ethical responsibilities of individual practitioners. To investigate the problems associated with developing and implementing a personal COE, we drafted an LA Practitioner COE based on other professional codes, and invited feedback from a range of learning analytics stakeholders and practitioners: ethicists, students, researchers and technology executives. Three main themes emerged from their reflections: 1. A need to balance real world demands with abstract principles, 2. The limits to individual accountability within the learning analytics space, and 3. The continuing value of debate around an aspirational code of ethics within the field of learning analytics.

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.

As higher education increasingly moves to online and digital learning spaces, we have access not only to greater volumes of student data, but also to increasingly fine-grained and nuanced data. A significant body of research and existing practice are used to convince key stakeholders within higher education of the potential of the collection, analysis and use of student data to positively impact on student experiences in these environments. Much of the recent focus in learning analytics is around predictive modeling and uses of artificial intelligence to both identify learners at risk, and to personalize interventions to increase the chance of success. In this paper we explore the moral and legal basis for the obligation to act on our analyses of student data. The obligation to act entails not only the protection of student privacy and the ethical collection, analysis and use of student data, but also, the effective allocation of resources to ensure appropriate and effective interventions to increase effective teaching and learning. The obligation to act is, however tempered by a number of factors, including inter and intra-departmental operational fragmentation and the constraints imposed by changing funding regimes. Increasingly higher education institutions allocate resources in areas that promise the greatest return. Choosing (not) to respond to the needs of specific student populations then raises questions regarding the scope and nature of the moral and legal obligation to act. There is also evidence that students who are at risk of failing often do not respond to institutional interventions to assist them. In this paper we build and expand on recent research by, for example, the LACE and EP4LA workshops to conceptually map the obligation to act which flows from both higher education's mandate to ensure effective and appropriate teaching and learning and its fiduciary duty to provide an ethical and enabling environment for students to achieve success. We exami

The growth of learning analytics as a means to improve student learning outcomes means that student data is being collected, analyzed, and applied in previously unforeseen ways. As the use of this data continues to shape academic and support interventions, there is increasing need for ethical reflection on operational approvals for learning analytics research. Though there are clear processes for vetting studies resulting in publication of student-gathered data, there is little comparable oversight of internally generated student-focused research. Increasingly, ethical concerns about the collection and harvesting of student data have been raised, but there is no clear indication how to address or oversee these ethical concerns. In addition, staff members who are not typical researchers may be less familiar with approvals processes and the need to demonstrate potential for harm, etc. If current trends point to a range of individuals harvesting and analyzing student data (mostly without students' informed consent or knowledge), how can the real danger of unethical behavior be curbed to mitigate the risk of unintended consequences? A systematic appraisal of the policy frameworks and processes of ethical review at three research institutions (namely, the University of South Africa, the Open University in the United Kingdom, and Indiana University in the United States) provides an opportunity to compare practices, values, and priorities. From this cross-institutional review, a working typology of ethical approaches is suggested within the scope of determining the moral intersection of internal student data usage and application.

In light of increasing concerns about surveillance, higher education institutions (HEIs) cannot afford a simple paternalistic approach to student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analyzed, used, and shared. It is clear from literature that basic opting in or out does not sufficiently address many of the complex issues in the nexus of privacy, consent, vulnerability, and agency. The notion of vulnerability (institutional and individual) allows an interesting and useful lens on the collection and use of student data. Though both institutional and individual vulnerability need to be considered, this paper focuses specifically on student vulnerability. In this conceptual article, we explore student vulnerability in the nexus between realizing the potential of learning analytics; the fiduciary duty of HEIs in the context of their asymmetrical information and power relations with students; and the complexities surrounding student agency in learning analytics. This article expands on an earlier framework developed by Prinsloo and Slade (2015). It aims to explore ways to decrease student vulnerability, increase their agency, and empower them as participants in learning analytics — moving them from quantified data objects to qualified and qualifying selves.

Learning analytics is an emerging but rapidly growing field seen as offering unquestionable benefit to higher education institutions and students alike. Indeed, given its huge potential to transform the student experience, it could be argued that higher education has a duty to use learning analytics. In the flurry of excitement and eagerness to develop ever slicker predictive systems, few pause to consider whether the increasing use of student data also leads to increasing concerns. This chapter argues that the issue is not whether higher education should use student data, but under which conditions, for what purpose, for whose benefit, and in ways in which students may be actively involved. The authors explore issues including the constructs of general data and student data, and the scope for student responsibility in the collection, analysis and use of their data. An example of student engagement in practice reviews the policy created by the Open University in 2014. The chapter concludes with an exploration of general principles for a new deal on student data in learning analytics.

Though concern about student attrition and failure is not a new phenomenon, higher education institutions (HEIs) have struggled to significantly reduce the revolving door syndrome. Open distance learning higher education is particularly susceptible to high student attrition. Despite a great deal of research into the student journey and factors impacting on likely success, we are not necessarily closer to understanding and being able to mitigate against student attrition. Learning analytics as emerging discipline and practice promises to help penetrate the fog… This case study describes work undertaken at the Open University in the UK to investigate how a learning analytics approach allows the University to provide timely and appropriate student support in a cost-effective manner. It includes a summary of the establishment of curriculum-based student support teams and a framework which defines more standardised student support informed by both student data and an enhanced knowledge of the curriculum. The primary aim of student support teams is to proactively support students through their study journey and to optimise their chances of reaching their declared study goals. Higher education institutions (HEIs) are making increasing use of learning analytics to support delivery of timely and relevant student support. The Open University in the UK, like other HEIs, knows a great deal about its students before they start to study and is able to track student behaviours once study has begun. Until recently, the university has not taken full advantage of the additional insight offered by such information. This paper describes the framework of support interventions established for all student support teams and describes the learning analytics approach used to support that framework.

Amidst vast changes sweeping the higher education landscape, there is an increasing need to use data to increase the effectiveness of teaching and learning, and subsequently, ensure accountability and efficiency in an increasingly resource-constrained and competitive higher education landscape (Altbach et al, 2009). Learning analytics as an emerging discipline and practice promises to contribute to more effective teaching, learning and resource allocation through the collection, analysis and use of student data (Prinsloo & Slade, 2014). As teaching and learning move progressively online and digital, the amount of student data increases exponentially, opening opportunities for data-informed strategies and pedagogies. Though there is no doubt that the collection, analysis and use of student digital data do offer huge potential, there are also a number of risks and ethical challenges such as the belief that data is neutral; the role of algorithms and the algorithmic turn in higher education; the assumptions and epistemologies informing the collection and analysis and use of data; and the increasing possibilities for discriminating against already vulnerable and at-risk students (Slade & Prinsloo, 2013; Prinsloo & Slade, 2014). This paper follows Prinsloo (2014) who proposes that ‘Learning analytics are a structuring device, not neutral, informed by current beliefs about what counts as knowledge and learning, coloured by assumptions about gender/race/class/capital/literacy and in service of and perpetuating existing or new power relations.’ Though the collection, analysis and use of student digital data aims to decrease students’ vulnerability and risks of failing or dropping out, there is also the possibility that in the light of the asymmetrical power relationship between student and institutions of higher learning, students’ vulnerability may actually be exacerbated. As higher education institutions (HEIs) optimise the potential of learning analytics, this paper prop

As in many fields, most papers in the learning analytics literature report success or, at least, read as if they are reporting success. This is almost certainly not because learning analytics research and activity are always successful. Generally, we report our successes widely, but keep our failures to ourselves. As Bismarck is alleged to have said: it is wise to learn from the mistakes of others. This workshop offers an opportunity for researchers and practitioners to share their failures in a lower-stakes environment, to help them learn from each other’s mistakes.

Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around the extent to which individuals are routinely and progressively tracked as they engage online. The Internet, the very thing that promised to open up possibilities and to break down communication barriers, now threatens to narrow it again through the panopticon of mass surveillance. Within higher education, our assumptions and understanding of issues surrounding student attitudes to privacy are influenced both by the apparent ease with which the public appear to share the detail of their lives and our paternalistic institutional cultures. As such, it can be easy to allow our enthusiasm for the possibilities offered by learning analytics to outweigh consideration of issues of privacy. This paper explores issues around consent and the seemingly simple choice to allow students to opt-in or opt-out of having their data tracked. We consider how 3 providers of massive open online courses (MOOCs) inform users of how their data is used, and discuss how higher education institutions can work toward an approach which engages and more fully informs students of the implications of learning analytics on their personal data.

The Open University (OU) is a large, open distance learning institution with more than 200,000 students. In common with many other higher education institutions (HEIs), the University is looking more closely at its use of learning analytics. Learning analytics has been defined as the collection and analysis of data generated during the learning process in order to improve the quality of learning and teaching (Siemens, Dawson, & Lynch, 2013). In the context of the Open University, learning analytics is the use of raw and analysed student data to, inter alia, proactively identify interventions which aim to support students in completing their study goals. Such interventions may be designed to support students as individuals as well as at a cohort level. The use of a learning analytics approach to inform and provide direction to student support within the Open University is relatively new and, as such, existing policies relating and referring to potential uses of student data have required fresh scrutiny to ensure their continued relevance and completeness (Prinsloo & Slade, 2013). In response, The Open University made the decision to address a range of ethical issues relating to the University’s approach to learning analytics via the implementation of new policy. In order to formulate a clear policy which reflected the University’s mission and key principles, it was considered essential to consult with a wide range of stakeholders, including students.

“Are students walking around with invisible triage tags attached, that only lecturers can see? Is this fair? Or is it just pragmatic? Like battlefield medical attention, lecturers’ attention is finite. And as class sizes and workloads increase, it is becoming scarcer” (Manning, 2012) It is difficult to understate the scope and impact of the changes facing international and national higher education. Terms such as “disruption” and “innovation”, “disaggregation”, the “unbundling and unmooring”, “revolution”, and “crisis” have become endemic to discourses on the current and future states of higher education. Against this backdrop, higher education institutions increasingly need to make strategic decisions regarding opportunities and alleviating risk. Risk within higher education both mirrors the broader societal dimensions of risk, and also presents additional aspects including the danger of obsolescence, changing funding regimes, the impact of technology on content, assessment and the role of faculty, the increasing diversification of forms of higher education and student populations, and concerns about student success and retention. Within this context, higher education and in particular open distance and elearning (ODeL) increasingly relies on the harvesting, analysis and use of available data to inform strategic decisions regarding enrolment, marketing, curriculum development, the appointment of staff, student assessment and increasingly, strategies that inform initiatives to increase student retention and success. The harvesting and analysis of student data therefore offers opportunities for higher education institutions to respond, timeously and appropriately, to identifying students who are at risk of failing or dropping out. The opportunities offered by learning analytics have, however, also brought to the fore concerns regarding a number of issues such as governmentality, data privacy, consent and other ethical issues and challenges. The central question this