Testing governance: the laboratory lives and methods of policy innovation labs

Public and social innovation labs have proliferated globally. By combining resources and practices from politics, data analysis, media, design, and digital innovation, labs act as experimental R&D labs and practical ideas organizations for solving social and public problems, located in the borderlands between sectors, fields and disciplinary methodologies. Labs are making methods such as data analytics, design thinking and experimentation into a powerful set of governing resources. This working paper analyses the key methods and messages of the labs field, in particular by investigating the documentary history of Futurelab, a prototypical lab for education research and innovation that operated in Bristol, UK, between 2002 and 2010, and tracing methodological continuities through the current wave of lab development. Centrally, the working paper explores Futurelab’s contribution to the production and stabilization of a ‘sociotechnical imaginary’ of the future of education specifically, and to the future of public services more generally, and analyses how such an imaginary was embedded in its ‘laboratory life,’ established through its organizational networks, and operationalized in its methods of research and development as well as its modes of communication. By taking a historical and genealogical perspective to the study of labs, it becomes clear how their current concerns, ideas and methods have been formed over time in concrete organizational sites and inter-organizational networks. The purpose of the working paper is not to evaluate labs’ methods, but to explore the longer continuities of thinking that animate them, their inter-organizational and ideational connections, and in particular to examine the imaginaries or visions of the future of public and social services that they share. Innovation labs are proposing to introduce more experimental methods into strategies of contemporary governance, and testing out new practical ideas and techniques for managing relations between the state and its citizens. Conducting detailed genealogical case studies and situated ethnographic research of the laboratory life within specific labs, as well as documentary analyses of their products and resources, are necessary next steps in social scientific and policy studies of innovation labs

Who owns educational theory? Big data, algorithms and the expert power of education data science

‘Education data science’ is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University’s laboratory for research and development in learning analytics, and the Center for Digital Data, Analytics and Adaptive Learning, a big data research centre of the commercial education company Pearson. These institutions are becoming methodological gatekeepers with the capacity to conduct new forms of educational research using big data and algorithmic data science methods. The central argument is that as educational data science has migrated from the academic lab to the commercial sector, ownership of the means to produce educational data analyses has become concentrated in the activities of for-profit companies. As a consequence, new theories of learning are being built-in to the tools they provide, in the shape of algorithm-driven technologies of personalization, which can be sold to schools and universities. The paper addresses two themes of this special issue: (1) how education is to be theorized in relation to algorithmic methods and data scientific epistemologies and (2) how the political economy of education is shifting as knowledge production becomes concentrated in data-driven commercial organizations

Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems

Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management

Imagining machine vision: Four visual registers from the Chinese AI industry

Machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the national AI market, and technologies like face recognition, object tracking and automated driving play a central role in surveillance systems and social governance projects relying on the large-scale collection and processing of sensor data. Like other novel articulations of technology and society, machine vision is defined, developed and explained by different actors through the work of imagination. In this article, we draw on the concept of sociotechnical imaginaries to understand how Chinese companies represent machine vision. Through a qualitative multimodal analysis of the corporate websites of leading industry players, we identify a cohesive sociotechnical imaginary of machine vision, and explain how four distinct visual registers contribute to its articulation. These four registers, which we call computational abstraction, human–machine coordination, smooth everyday, and dashboard realism, allow Chinese tech companies to articulate their global ambitions and competitiveness through narrow and opaque representations of machine vision technologies.publishedVersio

Critical data studies, abstraction and learning analytics: Editorial to Selwyn’s LAK keynote and invited commentaries

© 2019, UTS ePRESS. All rights reserved. This editorial introduces a special section of the Journal of Learning Analytics, for which Neil Selwyn’s keynote address to LAK ’18 has been written up as an article, “What’s the problem with learning analytics?” His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails

Consumer Health Informatics: Empowering Healthy-Lifestyle-Seekers Through mHealth

People are at risk from noncommunicable diseases (NCD) and poor health habits, with interventions like medications and surgery carrying further risk of adverse effects. This paper addresses ways people are increasingly moving to healthy living medicine (HLM) to mitigate such health threats. HLM-seekers increasingly leverage mobile technologies that enable control of personal health information, collaboration with clinicians/other agents to establish healthy living practices. For example, outcomes from consumer health informatics research include empowering users to take charge of their health through active participation in decision-making about healthcare delivery. Because the success of health technology depends on its alignment/integration with a person's sociotechnical system, we introduce SEIPS 2.0 as a useful conceptual model and analytic tool. SEIPS 2.0 approaches human work (i.e., life's effortful activities) within the complexity of the design and implementation of mHealth technologies and their potential to emerge as consumer-facing NLM products that support NCDs like diabetes

Sociotechnical Systems through a Work System Lens :A Possible Path for Reconciling System Conceptualizations, Business Realities, and Humanist Values in IS Development

This position paper describes an approach that might increase the likelihood that the sociotechnical perspective will take its proper place in today’s world. This paper questions the clarity of the traditional STS notion of joint optimization of a social system and technical system. It explains how the integrated system view in work system theory (WST) and the work system method (WSM) might provide a more straightforward way to describe, discuss, and negotiate about sociotechnical systems. Using WST/WSM to bypass the effort of separately describing and jointly optimizing social and technical systems might make it easier to engage effectively in discussions that reconcile system conceptualizations, business realities, and humanist values in IS development