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Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML), and the provision of automated, mostly real-time feedback to students and instructors. Much of MMLA takes place in experimental and laboratory settings, researching students' learning in in-between spaces--between research and classroom application, in-between students' learning in private and public spaces as researchers track students' learning both in their use of social media and connected devices, and through the use of context-aware and adaptive devices; and lastly, in-between respecting students' privacy while increasingly using intrusive technologies. This study seeks to establish what is known about MMLA in terms of rationale for applications, the nature and scope of data collected, the study contexts, evidence of commercial interests and/or downstream uses of students' data, and consideration of ethics, privacy, and the protection of student data. This systematic review analysed 192 articles using a search string consisting of various combinations of multimodal (data) and learning analytics. The main findings include, inter alia, that though MMLA provide insights into learning and teaching, there is little evidence of MMLA findings successfully being applied to classroom settings, at scale. Given that the nature of MMLA research often necessitates the use of a range of (intrusive) sensors and recording technologies and can include children in its samples; the encroachment of students' right to privacy is a huge concern that is not addressed. There is also a need to reconsider the rationale for collecting multimodal data, the conditions under which such data collection will be ethical and in service of students' wellness, and the boundaries that should protect their (multimodal) data.

While learning analytics (LA) has been highlighted as a field aiming to address systemic equity and quality issues within educational systems between and within regions, to date, its adoption is predominantly in the Global North. Since the Society for Learning Analytics Research (SoLAR) aspires to be international in reach and relevance, and to increase the diversity and inclusivity of the SoLAR community, it is pertinent to look at learning analytics use in higher education institutions in Africa. This paper reports the first scoping review of research in the field of LA conducted on the African continent. The coding and analysis show that LA research is still in its infancy on the African continent with only 15 studies, overwhelmingly from South Africa. The study also revealed several challenges, such as limited technical support and access to LMSs, the limited visibility of African scholars at SoLAR events and publication venues, and the limited focus on interventions that involve stakeholders. The article concludes with several propositions and provocations to advance the adoption of LA in Africa and open up space for critical conversations about the potential of learning analytics in contexts with characteristics different than those found in the Global North.

Learning analytics offer centralization of a particular understanding of learning, teaching, and student support alongside data-informed insight and foresight. As such, student-related data in higher education can be imagined and enacted as a 'data frontier' in which the data gaze is expanding, intensifying, and performing new meanings and practices. But the gaze is not necessarily one-way. In this article we conceptualize different ways the data gaze might be returned. Drawing on more-than-human theorizing, we map three descriptive accounts to better understand the tangle of datafied bodies complicit in the gaze of learning analytics and to inform how the data gaze may be re-shaped, re-directed, and re-storied as forms of data activism. This includes uncovering what algorithms do, improvising passages, and talking back with data-bodies. Such theorization offers insights for new modes of proactive data activism to address the problematics and opportunities of the intensified datafication of learning analytics platforms.

This article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such as AI. While there is an expanding body of research on AI in education (AIEd), this conceptual article proposes that combinations of human-algorithmic decision-making systems need careful and critical consideration, not only for their potential, but also for their appropriateness and ethical considerations. We operationalize a framework designed to consider robot autonomy at four key events in students' learning journeys, namely (1) admission and registration; (2) student advising and support; (3) augmenting pedagogy; and (4) formative and summative assessment. We conclude the article by providing pointers for operationalizing options in human-algorithmic decision-making in distributed learning contexts.

Higher education institutions are moving to design and implement teacher-facing learning analytics (LA) dashboards with the hope that instructors can extract deep insights about student learning and make informed decisions to improve their teaching. While much attention has been paid to developing teacher-facing dashboards, less is known about how they are designed, implemented and evaluated. This paper presents a systematic literature review of existing studies reporting on teacher-facing LA dashboards. Out of the 1968 articles retrieved from several databases, 50 articles were included in the final analysis. Guided by several frameworks, articles were coded based on the following dimensions: purpose, theoretical grounding, stakeholder involvement, ethics and privacy, design, implementation, and evaluation criteria. The findings show that most dashboards are designed to increase teachers' awareness but with limited actionable insights to allow intervention. Moreover, while teachers are involved in the design process, this is mainly at the exploratory/problem definition stage, with little input beyond this stage. Most dashboards were prescriptive, less customisable, and implicit about the theoretical constructs behind their designs. In addition, dashboards are deployed at prototype and pilot stages, and the evaluation is dominated by self-reports and users' reactions with limited focus on changes to teaching and learning. Besides, only one study considered privacy as a design requirement. Based on the findings of the study and synthesis of existing literature, we propose a four-dimensional checklist for planning, designing, implementing and evaluating LA dashboards.

Background: The COVID-19 pandemic disrupted higher education in many ways, such as the move to Emergency Remote Online Teaching and Learning (EROTL), often including a move to online assessments and examinations. With evidence of increased academic dishonesty in unproctored online assessment, institutions sought ways to ensure academic and institutional integrity and reputation. In doing this, many institutions selected and implemented online proctoring solutions. Objectives: This article maps considerations of online proctoring solutions in the nexus between ensuring academic and institutional integrity and reputation, and addressing stakeholder concerns regarding invasive surveillance and the impacts on student privacy. Methods: The study involved a PRISMA-informed systematic review of three digital libraries, namely Clarivate's Web of Science, Elsevier's Scopus, and Springer's SpringerLink, for peer-reviewed journal articles and conference proceedings. After screening, a final corpus of 27 articles was analysed. Results and Conclusions: The findings include evidence that, in the midst of the COVID-19 pandemic, higher education institutions were largely influenced by cost, usability and efficiency in choosing online proctoring solutions to ensure academic and institutional integrity. Student privacy was either considered in terms of data protection and transparency, or not at all. This article aims to provide valuable insight into the criteria used to select online proctoring solutions to ensure academic and institutional integrity in online examination environments. Student privacy appears not to have the consideration it warrants.

Much research in face-to-face contexts outlines the importance of early adjustment on students' higher education experiences. However, few studies have replicated this research in distance learning contexts to unpack the early multifaceted adjustments associated with studying in absence of a physical campus. This is particularly needed from a Global South perspective, where countries like South Africa have become regional hubs for distance learners. To explore distance learners' adjustment experiences, this study analysed results from a Student Adaptation to College Questionnaire (SACQ) with 320 distance learners at the University of South Africa, mixed with qualitative thematic analysis of open-ended questions. The results outlined key factors that impact distance learning experiences for students in South Africa, including demographic variables, class, language, and access to resources. These findings, compared with similar work in face-to-face contexts, suggest areas in need of additional support from distance education providers in South Africa and beyond.

Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, e.g., student privacy. This article maps some limitations of technological approaches that ensure student data privacy in learning analytics from a critical data studies (CDS) perspective. In this conceptual article, we map the claims, grounds and warrants of technological solutions to maintaining student data privacy in learning analytics. Our findings suggest that many technological solutions are based on assumptions, such as that individuals have control over their data ('data as commodity'), which can be exchanged under agreed conditions, or that individuals embrace their personal data privacy as a human right to be respected and protected. Regulating student data privacy in the context of learning analytics through technology mostly depends on institutional data governance, consent, data security and accountability. We consider alternative approaches to viewing (student) data privacy, such as contextual integrity; data privacy as ontological; group privacy; and indigenous understandings of privacy. Such perspectives destabilise many assumptions informing technological solutions, including privacy enhancing technology (PET).

Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers "outside" of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for learning analytics in African higher education is therefore needed. In this paper, we map the current state of legal and regulatory environments and frameworks on privacy to establish their implications for learning analytics. This scoping review of privacy regulations in 32 African countries, complemented by 15 scholarly papers, revealed that there are numerous national and regional legislation and regulatory frameworks, providing clear pointers pertaining to (student) data privacy to governments, higher education institutions and researchers. As such, the findings of this research have implications for African higher education to ensure not only legal compliance but also to oversee and safeguard student data privacy as part of their fiduciary duty. This research provides crucial insights regarding the importance of context for thinking about the expansion and institutional adoption of learning analytics.

Keynotes fulfill a particular function in the planning and compiling of a conference program, and these individuals are invited for a number of reasons -- to lend political and/or scholarly gravitas to the event, to stimulate and enlighten, and/or to provoke. Keynotes are also often outsiders to a particular field, and an invitation to deliver a keynote heralds an understanding that, despite being outsiders, their insights are considered valuable to the field. In his keynote, Selwyn embraced the identity of an outsider and, as such, claims the rights and privileges, however temporarily, to question and provoke. In order to situate his commentary differently than the others, Paul Prinsloo reflects and comments on Selwyn's keynote from the perspective of Critical Data Studies (CDS). Prinsloo's approach is not accidental, but primarily based on Selwyn's own claim that his keynote was informed by CDS and secondly, on Prinsloo's own orientation and location within the context of CDS. In reflecting and commenting on Selwyn's keynote, Prinsloo feels it will therefore be appropriate to consider first whether Selwyn's approach to problematizing learning analytics fit within the general context of CDS. Prinsloo then shares some reflections on Selwyn's keynote and specifically his wish list for learning analytics.

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.

Open Educational Resources (OER) in higher education cannot be put into practice without considering institutional contexts, which differ not only globally but also within the same country. Each institutional context provides educators with opportunities or limitations where Open Educational Practices (OEP) and OER for teaching and learning are involved. As part of a broader research project, and as a follow-up to national perspectives, an international comparison was conducted, based on institutional cases of nine different higher education systems (Australia, Canada, China, Germany, Japan, South Africa, South Korea, Spain, Turkey). Aspects regarding the availability of infrastructure and institutional policies for OER, as well as the existence of measures directed at OER quality assurance and at the promotion of the development and use of OER were covered. The resulting theoretical contribution sheds light on an international comparative view of OER and points towards country-specific trends, as well as differences among institutions. These aspects could provide an impetus for the development of institutional guidelines and measures. In line with international literature on the topic, recommendations are derived to promote/enhance the use of OER in teaching and learning in higher education at the institutional level.

Distance education celebrates its humanitarian mission of providing opportunities for disadvantaged and marginalized individuals who do not have access to traditional campus-based higher education. Large enrolments of students necessitate an industrialized approach in planning, design, and delivery informed by a normative assumption of ableism. In the context of post-apartheid South Africa, distance education fulfills a particular important role in ensuring access to education for all. This scoping review addressed two questions: "What is currently known, in the context of distance education in South Africa, about the educational experiences of SWDs?" and "How should we understand and respond to these experiences?" The scoping study shows, inter alia, that while there is an institutional commitment to providing equitable learning experiences, the lived experiences of students with disabilities provide evidence of their invisibility in the design, planning, and delivery of learning. The study concludes with some recommendations and provocations.

This paper explores faculty's perspectives and use of open educational resources (OER) and their repositories across different countries by conducting a multiple case study to find similarities and differences between academics' awareness, perceptions and use of OER, as well as examining related aspects of institutional policy and quality that may influence individual views. Data were collected through nine expert reports on each country studied (Australia, Canada, China, Germany, Japan, South Africa, South Korea, Spain and Turkey) and were analyzed through qualitative content analysis using thematic coding. Findings show the impact on individual OER adoption with regard to the individual control of diverse factors by faculty members; of institutional policies and quality measures on the externally determined factors (by the institution); and of institutional professional development and provision of incentives in more internally determined factors (by the faculty members themselves). These findings carry implications for higher education institutions around the world in their attempt to boost OER adoption by faculty members.

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.

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential. The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes. This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.

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 additional support to alleviate this risk.

Student success and retention is a primary goal of higher education institutions across the world. The cost of student failure and dropout in higher education is multifaceted including, amongst other things, the loss of revenue, prestige, and stakeholder trust for both institutions and students. Interventions to address this are complex and varied. While the dominant thrust has been to investigate academic and non-academic risk factors thus applying a "risk" lens, equal attention should be given to exploring the characteristics of successful students which expands the focus to include "requirements for success". Based on a socio-critical model for understanding of student success and retention, the University of South Africa (Unisa) initiated a pilot project to benchmark successful students' habits and behaviours using a tool employed in business settings, namely Shadowmatch®. The original focus was on finding a theoretically valid measured for habits and behaviours to examine the critical aspect of student agency in the social critical model. Although this was not the focus of the pilot, concerns regarding using a commercial tool in an academic setting overshadowed the process. This paper provides insights into how academic-business collaboration could allow an institution to be more dynamic and flexible in supporting its student population.

Student data, whether in the form of engagement data, assignments or examinations, form the foundation for assessment and evaluation in higher education. As higher education institutions progressively move to blended and online environments, we have access to, not only more data than before, but also a greater variety of demographic and behavioural data. While the notion of 'student-centred' is well-established in the discourses and practices surrounding assessment and evaluation, the concept of "student-centred learning analytics" is yet to be fully realised by the sector. This article explores and extends this debate by introducing the teachings of Freire as a framework to examine the potential to include students as partners in the collection, analysis and use of "their" data. The exclusion of students in much of current learning analytics practices, as well as defining categories of analysis and making sense of (their) learning, not only impoverishes our (and their) understanding of the complexities of learning and assessment, but may actually increase vulnerabilities and perpetuate bias and stereotypes. In acknowledging the voice and agency of students, and recentring them as data owners, rather than data objects, learning analytics can realise its transformative potential -- for students and institutions alike.

'Data as technology' has always been, and continues to be an essential part of the structuring of South African society and education, during and post-colonialism and post-apartheid. In the reconfiguration of South African education post-apartheid, student data constitutes a data frontier as un-mapped, under-utilised and ready for the picking. This article maps the data frontier in the nexus of higher education in the global/colonial present and the data imaginary that provides a particular vision in service of a neoliberal discursive position and ideological orientation. As such, the data frontier acts as 'generative matrix' for educational policy attempting to address the legacies of colonialism and apartheid. The value contribution of this article lies in its positioning of the data imaginary in the context of a neoliberal approach to education in the context of the Global South.

This article examines developments in the 'smart classroom' as a new frontier for the university. It provides a conceptual map of the scope and limitations of smart classrooms, contextualized to smart university initiatives. First, it introduces the notion of 'smart' technology in cities, campuses, and classrooms. Next, it examines how the smart classroom is conceptualized, its present applications, and how it may be utilized with or without integration into the smart campus. Finally, it explores the ethical implications of the smart classroom. It concludes by recommending new regulations for smart classrooms and provides examples of classroom technologies constructive to the pedagogical process.

Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment "of" and "for" learning. As such it has the potential to deliver value in the form of (1) "understanding" student learning, (2) "analysing" learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) "predicting" students-at-risk (or identifying where students have specific learning needs), and (4) "prescribing" elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students' chances of success, deepening the understanding of learning.

Data--their collection, analysis and use--have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research focus and practice. With (higher) education having access to more student data, greater variety and nuanced/granularity of data, as well as collecting and using real-time data, it is crucial to consider the data imaginary in higher education, and, specifically, analytics as performative politics. Data and data analyses are often presented as representing "reality" and, as such, are seminal in institutional "truth-making," whether in the context of operational or student learning data. In the broader context of critical data studies (CDS), this social cartography examines and maps the "data frontier" and the "data gaze" within the context of the dominant narrative of evidence-based management and the data imaginary in higher education. Following an analysis of the main assumptions in evidence-based management and the power of metrics, this paper presents a social cartography of data analytics not only as representational, but as actant, and as performative politics.

Students' physical and digital lives are increasingly entangled. It is difficult to separate students' digital well‐being from their offline well‐being given that artificial intelligence increasingly shapes both. Within the context of education's fiduciary and moral duty to ensure safe, appropriate and effective digital learning spaces for students, the continuing merger between artificial intelligence and learning analytics not only opens up many opportunities for more responsive teaching and learning but also raises concerns, specifically for previously disadvantaged and vulnerable students. While digital well‐being is a well‐established research focus, it is not clear how AI‐Powered Educational Decision Support Systems (AI‐EDSS) might impact on the inherent, situational and pathogenic vulnerability of students. In this conceptual paper, we map the digital well‐being of previously disadvantaged and vulnerable students in four overlapping fields, namely (1) digital well‐being research; (2) digital well‐being research in education; (3) digital well‐being research in learning analytics; and (4) digital well‐being in AI‐informed educational contexts. With this as the basis, we engage with six domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being and provide pointers for safeguarding and enhancing disadvantaged and vulnerable student digital well‐being in AI‐EDSS. Practitioner notesWhat is already known about this topic Digital well‐being research is a well‐established focus referring to the impact of digital engagement on human well‐being.Digital well‐being is effectively inseparable from general well‐being as it is increasingly difficult to disentangle our online and offline lives and, as such, inherently intersectional.Artificial Intelligence shows promise for enhancing human digital well‐being, but there are concerns about issues such as privacy, bias, transparency, fairness and accountability.The notion of 'vulnerable individuals' includes individuals who were previously disadvantaged, and those with inherent, situational and/or pathogenic vulnerabilities.While current advances in AI‐EDSS may support identification of digital wellness, proxies for digital wellness should be used with care.What this study contributes An overview of digital well‐being research with specific reference how it may impact on vulnerable students.Illustrates specific vulnerabilities in five domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being selected for their significance in online learning environments.Pointers for the design and implementation of fair, ethical, accountable, and transparent AI‐EDSS with specific reference to vulnerable students.Implications for practice and/or policy Fairness, equity, transparency and accountability in AI‐EDSS affect all students but may have a greater (positive or negative) impact on vulnerable students.A critically informed understanding of the nature of students' vulnerability—whether as inherent, situational and/or pathogenic, as well as temporal/permanent aspects—is crucial.Since AI‐EDSS can exacerbate existing vulnerabilities resulting in pathogenic vulnerability, care is needed when designing AI‐EDSS. [ABSTRACT FROM AUTHOR]