Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically

Learning and Work: Professional Learning Analytics

Learning for work takes various forms, from formal training to informal learning through work activities. In many work settings, professionals collaborate via networked environments leaving various forms of digital traces and “clickstream” data. These data can be exploited through learning analytics (LA) to make both formal and informal learning processes traceable and visible to support professionals with their learning. This chapter examines the state-of-the-art in professional learning analytics (PLA) by considering how professionals learn, putting forward a vision for PLA, and analyzing examples of analytics in action in professional settings. LA can address affective and motivational learning issues as well as technical and practical expertise; it can intelligently align individual learning activities with organizational learning goals. PLA is set to form a foundation for future learning and work

Towards data exchange formats for learning experiences in manufacturing workplaces

Manufacturing industries are currently transforming, most notably through the introduction of advanced machinery and increasing degrees of au- tomation. This has caused a shift in skills required, calling for a skills gap to be filled. Learning technology needs to embrace this change and with this contri- bution, we propose a process model for learning by experience to understand and explain learning under these changed conditions. To put this process into practice, we propose two interchange formats for capturing, sharing, and re- enacting pervasive learning activities and for describing workplaces with in- volved things, persons, places, devices, apps, and their set-up

Learning Analytics Community Exchange: Evidence Hub

This poster sets out the background and development of the LACE Evidence Hub, a site that gathers evidence about learning analytics in an accessible form. The poster also describes the functionality of the site, summarises its quantitative and thematic content to date and the state of evidence. In addition, it encourages people to add to and make use of the Hub

Fluid learning: vision for lifelong learning in 2030

This paper provides a vision of what we term ‘fluid learning’ through which autonomous learners make choices about their own learning. This vision is critical because it equips European citizens to live in a global context where knowledge and work is changing so rapidly that people have to learn continually. Fluid learning is suited to a world that has seen a radical change in cultural perceptions of learner agency and learner-teacher roles, associated with changes in technology. After completing compulsory education, the focus of each learner moves from learning pre-defined knowledge to filling gaps between areas of knowledge, integrating different areas of expertise, as well as learning new knowledge. People do not turn automatically to formal institutions for large blocks of learning. Instead they consider it natural to make use of open learning resources and open courses, making their own decisions about what to learn, when and how. Learners naturally employ open learning practices, creating new knowledge for future learners to benefit from. They expect to contribute to the learning of others as well as learning themselves, viewing themselves as the experts in their own situation. In some cases they may elect to take a short formal course, but this is always for a specific reason rather than as a cultural norm. Rather than managing multiple identities in the different groups/communities to which they belong, they see their unique identity as a unifying factor that integrates their activities in various groups, including work and leisure groups that they move easily between. In doing so they accrue new knowledge, integrating it with their current understanding, such that their expertise changes dynamically to match their current needs. The vision requires significant cultural change in European society by 2030

Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

What are the New and Emerging Areas of HR and Talent Management Practices to Enhance the Productivity and the Business Outcome?

Question: What are the new and emerging areas of HR and talent management practices that we need to start paying attention to in order to enhance the productivity and the business outcome

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin