Simon Knight

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip on how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organise (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the Internet during exams.

People are increasingly exposed to conflicting health information and must navigate this information to make numerous decisions, such as which foods to consume, a process many find difficult. Although some consumers attribute these disagreements to aspects related to uncertainty and complexity of research, many use a narrower set of credibility-based explanations. Experts’ views on disagreements are underinvestigated and lack explicit identification and classification of the differences in causes for disagreement. Consequently, there is a gap in existing literature to understand the range of reasons for these contradictions. Combining the findings from a literature study and expert interviews, a taxonomy of disagreements was developed. It identifies 10 types of disagreement classified under three dimensions: informant-, information- and uncertainty-related causes for disagreement. The taxonomy may assist with adoption of more effective strategies to deal with conflicting information and contributes to research and practice of science communication in the context of disagreement.

We draw on recent accounts of social epistemology to present a novel account of epistemic cognition that is ‘socialised’. In developing this account we foreground the: normative and pragmatic nature of knowledge claims; functional role that ‘to know’ plays when agents say they ‘know x’; the social context in which such claims occur at a macro level, including disciplinary and cultural context; and the communicative context in which such claims occur, the ways in which individuals and small groups express and construct (or co-construct) their knowledge claims. We frame prior research in terms of this new approach to provide an exemplification of its application. Practical implications for research and learning contexts are highlighted, suggesting a re-focussing of analysis on the collective level, and the ways knowledge-standards emerge from group-activity, as a communicative property of that activity.

There is a well-established literature examining the relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment. Learning Analytics (LA) is a new assessment technology and should engage with this literature since it has implications for when and why different LA tools might be deployed. This paper discusses these issues, relating them to an example construct, epistemic beliefs – beliefs about the nature of knowledge – for which analytics grounded in pragmatic, sociocultural theory might be well placed to explore. This example is particularly interesting given the role of epistemic beliefs in the everyday knowledge judgements students make in their information processing. Traditional psychological approaches to measuring epistemic beliefs have parallels with high stakes testing regimes; this paper outlines an alternative LA for epistemic beliefs which might be readily applied to other areas of interest. Such sociocultural approaches afford opportunity for engaging LA directly in high quality pedagogy.

Learning Analytics is an emerging research field and design discipline which occupies the ‘middle space’ between the learning sciences/educational research, and the use of computational techniques to capture and analyse data (Suthers and Verbert, 2013). We propose that the literature examining the triadic relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching) and assessment provide critical considerations for bounding this middle space. We provide examples to illustrate the ways in which the understandings of particular analytics are informed by this triad. As a detailed worked example of how one might design analytics to scaffold a specific form of higher order learning, we focus on the construct of epistemic beliefs: beliefs about the nature of knowledge. We argue that analytics grounded in a pragmatic, sociocultural perspective are well placed to explore this construct using discourse-centric technologies. The examples provided throughout this paper, through emphasising the consideration of intentional design issues in the middle space, underscore the “interpretative flexibility” (Hamilton & Feenberg, 2005) of new technologies, including analytics.

The issue of the epistemological implications of our social and technical interactions with information is the subject of this essay. This will be specified by looking at the role of the search engine as an informant, offering testimonial knowledge on a query; at the question of how the receiver of testimony should be taken into account by those giving the information; and how we should deal with multiplicity of perspectives, or indeed gaps in our knowledge. We should seek to understand the nature of ‘knowledge’, and how informants – including non-human informants – mediate our understanding of the world around us, and have always done so. This essay turns to these questions, discussing some issues with researching technological changes, and then what role search functions fulfil, and how such functions affect our own understanding of ‘knowledge’. Such an analysis has profound implications, for example in education. Under what circumstances do we accept that students ‘know’ something; how we do we decide that they know (that is, how do educators claim knowledge on their student’s knowledge states); but also what sort of knowledge is important important to know in such a situation, these are all important questions. Furthermore, how we think about the future of such technology and the ways that technology might change what we know (for better or worse) is important.