Concerns about the lack of impact of learning analytics (LA) research has been part of the evolution of the field since its emergence as a research focus and practice in 2011. The preponderance of small-scale and exploratory nature of much of LA research are well-documented as contributing factors to the lack of generalisability, transferability, replicability and scalability. Through an analysis of 144 full research papers published in the conference proceedings of the Learning Analytics & Knowledge (LAK) Conference '22, 23 and 24, this paper provides an overview of the extent and contours of the lack of generalisability in LA research and pointers for making LA research more generalisable. The inductive and deductive analysis of the recent three LAK conferences provide evidence that a significant percentage (46%) of the corpus papers do not refer at all to generalisability or transferability, while few papers report on the scalability of their research findings. While the crisis of replicability/reproducibility is a wider concern in the broader context of research, considering and reporting on generalisability and transferability is integral to the scientific rigour. We conclude our paper with a range of pointers for addressing the lack of generalisability in LA research including, but not limited to expanding data, methodological adaptation and the potential of open science.

While learning analytics frameworks precede the official launch of learning analytics in 2011, there has been a proliferation of learning analytics frameworks since. This systematic review of learning analytics frameworks between 2011 and 2021 in three databases resulted in an initial corpus of 268 articles and conference proceeding papers based on the occurrence of “learning analytics” and “framework” in titles, keywords and abstracts. The final corpus of 46 frameworks were analysed using a coding scheme derived from purposefully selected learning analytics frameworks. The results found that learning analytics frameworks share a number of elements and characteristics such as source, development and application focus, a form of representation, data sources and types, focus and context. Less than half of the frameworks consider student data privacy and ethics. Finally, while design and process elements of these frameworks may be transferable and scalable to other contexts, users in different contexts will be best-placed to determine their transferability/scalability.

Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data.

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.

While many strategies for protecting personal privacy have relied on regulatory frameworks, consent and anonymizing data, such approaches are not always effective. Frameworks and Terms and Conditions often lag user behaviour and advances in technology and software; consent can be provisional and fragile; and the anonymization of data may impede personalized learning. This paper reports on a dialogical multi-case study methodology of four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how the providers (1) define 'personal data' and whether they acknowledge a category of 'special' or 'sensitive' data; (2) address the issue and scope of student consent (and define that scope); and (3) use student data in order to inform pedagogy and/or adapt the learning experience to personalise the context or to increase student retention and success rates.

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.

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.

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.

Despite all the research on student retention and success since the first conceptual mappings of student success e.g. Spady [12], 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.