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Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is (not) collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions (and increasingly venture capital and learning management system providers) use to admit, steer, predict and prescribe students’ learning journeys.

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.

The article presents a socio-critical model and framework for understanding, predicting, and enhancing student success developed at the University of South Africa. An extensive literature review indicated that predominant models from international contact institutions were of partial application in this context. Integrating socio-critical, anthropological, and cultural theoretical perspectives, the model applies the key constructs of situated agency, capital, habitus, attribution, locus of control, and self-efficacy to both students and institutions in understanding success at each step of the student's journey. The model and framework, to be implemented incrementally during 2011, provide useful pointers for open distance learning and other institutions in pursuing greater student success.

It is clear now that open education is much more than a binary consideration of open versus closed but also includes \"opening.\" This book maps a range of different theoretical and practice-oriented approaches and proposals to (re)considering open education.

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.

Since its inception in 2011, Learning Analytics has matured and expanded in terms of reach (e.g., primary and K-12 education) and in having access to a greater variety, volume and velocity of data (e.g. collecting and analyzing multimodal data). Its roots in multiple disciplines yield a range and richness of theoretical influences resulting in an inherent theoretical pluralism. Such multi-and interdisciplinary origins and influences raise questions around which learning theories inform learning analytics research, and the implications for the field should a particular theory dominate. In establishing the theoretical influences in learning analytics, this scoping review focused on the Learning Analytics and Knowledge Conference (LAK) Proceedings (2011–2020) and the Journal of Learning Analytics (JLA) (2014–2020) as data sources. While learning analytics research is published across a range of scholarly journals, at the time of this study, a significant part of research into learning analytics had been published under the auspices of the Society of Learning Analytics (SoLAR), in the proceedings of the annual LAK conference and the field’s official journal, and as such, provides particular insight into its theoretical underpinnings. The analysis found evidence of a range of theoretical influences. While some learning theories have waned since 2011, others, such as Self-Regulated Learning (SRL), are in the ascendency. We discuss the implications of the use of learning theory in learning analytics research and conclude that this theoretical pluralism is something to be treasured and protected.

Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a data ecosystem with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader data ecologies, we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the different data interests vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.

This article maps considerations of inclusiveness and support for students with disabilities by reviewing articles within the field of learning analytics. The study involved a PRISMA-informed systematic review of two popular digital libraries, namely Clarivate’s Web of Science, and Elsevier’s Scopus for peer-reviewed journal articles and conference proceedings. A final corpus of 26 articles was analysed. Findings show that although the field of learning analytics emerged in 2011, none of the studies identified here covered topics of inclusiveness in education before the year of 2016. Screening also shows that learning analytics provides great potential to promote inclusiveness in terms of reducing discrimination, increasing retention among disadvantaged students, and validating particular learning designs for marginalised groups. Gaps in this potential are also identified. The article aims to provide valuable insight into what is known about learning analytics and inclusiveness and contribute knowledge to this particular nascent area for researchers and institutional stakeholders.

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.