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We propose that the design and implementation of effective Social Learning Analytics (SLA) present significant challenges and opportunities for both research and enterprise, in three important respects. The first is that the learning landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning. The second challenge is to identify different types of SLA and their associated technologies and uses. We discuss five categories of analytic in relation to online social learning; these analytics are either inherently social or can be socialised. This sets the scene for a third challenge, that of implementing analytics that have pedagogical and ethical integrity in a context where power and control over data are now of primary importance. We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature.

Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed feedback literacy. A previously published teacher feedback literacy competency framework has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of automated feedback (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: Which teacher feedback competencies are necessary for the skilled use of open AF tools? and What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies? We conduct an analysis of published evidence concerning teachers’ use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers’ feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of automated feedback literacy, that is, distinctive teacher competencies linked to the skilled use of open AF tools.

We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically , to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

It is widely documented that higher education institutional responses to the COVID-19 pandemic accelerated not only the adoption of educational technologies, but also associated socio-technical controversies. Critically, while these cloud-based platforms are capturing huge datasets, and generating new kinds of learning analytics, there are few strongly theorised, empirically validated processes for institutions to consult their communities about the ethics of this data-intensive, increasingly algorithmically-powered infrastructure. Conceptual and empirical contributions to this challenge are made in this paper, as we focus on the under-theorised and under-investigated phase required for ethics implementation, namely, joint agreement on ethical principles. We foreground the potential of ethical co-production through Deliberative Democracy (DD), which emerged in response to the crisis in confidence in how typical democratic systems engage citizens in decision making. This is tested empirically in the context of a university-wide DD consultation, conducted under pandemic lockdown conditions, co-producing a set of ethical principles to govern Analytics/AI-enabled Educational Technology (AAI-EdTech). Evaluation of this process takes the form of interviews conducted with students, educators, and leaders. Findings highlight that this methodology facilitated a unique and structured co-production process, enabling a range of higher education stakeholders to integrate their situated knowledge through dialogue. The DD process and product cultivated commitment and trust among the participants, informing a new university AI governance policy. The concluding discussion reflects on DD as an exemplar of ethical co-production, identifying new research avenues to advance this work. To our knowledge, this is the first application of DD for AI ethics, as is its use as an organisational sensemaking process in education.

Research into the teaching and assessment of student writing shows that many students find academic writing a challenge to learn, with legal writing no exception. Improving the availability and quality of timely formative feedback is an important aim. However, the time-consuming nature of assessing writing makes it impractical for instructors to provide rapid, detailed feedback on hundreds of draft texts which might be improved prior to submission. This paper describes the design of a natural language processing (NLP) tool to provide such support. We report progress in the development of a web application called AWA (Academic Writing Analytics), which has been piloted in a Civil Law degree. We describe: the underlying NLP platform and the participatory design process through which the law academic and analytics team tested and refined an existing rhetorical parser for the discipline; the user interface design and evaluation process; and feedback from students, which was broadly positive, but also identifies important issues to address. We discuss how our approach is positioned in relation to concerns regarding automated essay grading, and ways in which AWA might provide more actionable feedback to students. We conclude by considering how this design process addresses the challenge of making explicit to learners and educators the underlying mode of action in analytic devices such as our rhetorical parser, which we term algorithmic accountability.

Purpose - In this paper the aim is to present Debate Dashboard, an online collaborative platform designed to support distributed knowledge management and decision making. The platform integrates an argument mapping tool with visual widgets with the objective of enhancing collective sense-making and mutual understanding as well as to compensate for the costs of mediated communication in virtual collaborative environments.Design methodology approach - The design of Debate Dashboard is based on the theory of common ground according to which participants involved in a conversation build mutual understanding thanks to the exchange of different types of feedback. Using the concept of grounding cost, the authors identified several features of the Dashboard supposed to favour mutual understanding and knowledge sharing. Such features have been implemented through six visual widgets selected through a benchmarking of currently available visualization tools.Findings - The paper discusses the limitations and advantages of online argumentation to support online discussions and presents a review of current visualization tools. The design of a new platform able to integrate online argumentation and visualization technologies is described and it is argued that Debate Dashboard will improve online collaboration in many respects especially in terms of supporting the construction of shared knowledge representations for geographically distributed collaborative teams.Originality value - First, the work adds to the debate on the development of online argumentation platforms by offering an alternative theoretical perspective based on language and conversational studies. Second, it proposes for the first time to integrate argumentation and visualization technologies in the same tool to create an augmented collaborative platform able to overcome the limitations of both traditional online collaboration technologies, such as forums and wikis, as well as the criticalities associated with the use of argumentation technologies.

There is a growing interest in involving students and teachers in the design of human-centered Learning Analytics (LA) systems to align them with authentic learning needs. Yet, limited prior research has explored the implications of integrating both students’ and teachers’ perspectives within a structured co-design process. To address this shortcoming in the literature, we report on a study that examined how undergraduate nursing students and teachers co-designed an AI-powered LA system to support post-debriefing reflection on teamwork and communication in the context of healthcare simulation. This qualitative study, using a co-design approach, examined the design process of an LA system from conceptualization to post-use evaluation. The study addressed two key questions: i ) What tensions emerge from the contrasting perspectives of students and teachers in the co-design an AI-powered LA system? and ii ) How do students and teachers perceive their joint participation in the co-design process? Three key design tension themes emerged from the contrasting perspectives of students and teachers: teaching–learning goals tension , privacy–utility tension , and human-AI guidance preferences tension . The collaborative design process revealed mutual benefits: students valued teachers’ guidance in refining ideas and aligning system goals with learning objectives, while teachers, initially cautious about student involvement, came to see co-design as an opportunity to empower students and deepen their own understanding of responsible data use in practice. These findings contribute to the broader understanding of co-design dynamics in educational technology, underscoring the importance of balanced stakeholder involvement in developing practical, context-aware LA systems.

Purpose Many of today’s information and technology systems and environments facilitate inquiry, learning, consciousness-raising and knowledge-building. Such platforms include e-learning systems which have learning, education and/or training as explicit goals or objectives. They also include search engines, social media platforms, video-sharing platforms, and knowledge sharing environments deployed for work, leisure, inquiry, and personal and professional productivity. The new journal, Information and Learning Sciences, aims to advance our understanding of human inquiry, learning and knowledge-building across such information, e-learning, and socio-technical system contexts. Design/methodology/approach This article introduces the journal at its launch under new editorship in January, 2019. The article, authored by the journal co-editors and all associate editors, explores the lineage of scholarly undertakings that have contributed to the journal's new scope and mission, which includes past and ongoing scholarship in the following arenas: Digital Youth, Constructionism, Mutually Constitutive Ties in Information and Learning Sciences, and Searching-as-Learning. Findings The article offers examples of ways in which the two fields stand to enrich each other towards a greater holistic advancement of scholarship. The article also summarizes the inaugural special issue contents from the following contributors: Caroline Haythornthwaite; Krista Glazewski and Cindy Hmelo-Silver; Stephanie Teasley; Gary Marchionini; Caroline R. Pitt; Adam Bell, Rose Strickman and Katie Davis; Denise Agosto; Nicole Cooke; and Victor Lee. Originality/value The article, this special issue, and the journal in full, are among the first formal and ongoing publication outlets to deliberately draw together and facilitate cross-disciplinary scholarship at this integral nexus. We enthusiastically and warmly invite continued engagement along these lines in the journal’s pages, and also welcome related, and wholly contrary points of view, and points of departure that may build upon or debate some of the themes we raise in the introduction and special issue contents.

This paper considers the role and impact of new and emerging e-Science tools on practice-led research in dance. Specifically, it draws on findings from the e-Dance project. This 2-year project brings together an interdisciplinary team combining research aspects of choreography, next generation of videoconferencing and human-computer interaction analysis incorporating hypermedia and nonlinear annotations for recording and documentation.