Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Socialising Epistemic Cognition

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

Thinking, Interthinking, and Technological Tools

Language use is widely regarded as an important indicator of high quality learning and reasoning ability. Yet this masks an irony: language is fundamentally a social, collaborative tool, yet despite the widespread recognition of its importance in relation to learning, the role of dialogue is undervalued in learning contexts. In this chapter we argue that to see language as only a tool for individual thought presents a limited view of its transformative power. This power, we argue, lies in the ways in which dialogue is used to interthink – that is, to think together, to build knowledge co-constructively through our shared understanding. Technology can play an important role in resourcing thinking through the provision of information, and support to provide a space to think alone. It can moreover provide significant support for learners to build shared representations together, particularly through giving learners access to a wealth of ‘given’ inter-related texts which resource the co-construction of knowledge

Creating a supportive environment for classroom dialogue

This chapter first considers the role of dialogue in classroom contexts, and the importance of open-ended dialogue in contrast to more traditional, closed questioning sequences. I briefly discuss the role of dialogue in individual psychological development, focussing on its importance for conceptual development in whole classes and small groups in the context of the classroom. A common – closed – sequence of classroom talk is first outlined, and then discussed in the context of ‘dialogic talk’ – talk which is more open, builds on prior knowledge, is supportive and collaborative in nature. The use of ‘exploratory talk’ – talk which focuses on the use of reasoning to build mutual understanding – is also outlined in this context. 1. What role does dialogue play in learning? 2. What form does dialogue typically take? 3. How can we make dialogue more effective? The second part of the chapter discusses some ways to promote effective dialogue in classroom contexts. Some suggestions for creating and identifying an effective environment for classroom talk are discussed. I highlight the importance of ‘ground rules’ for talk, and some key words teachers might look for and emphasise in encouraging the use of ‘exploratory talk’. I then discuss some ideas for ways to start effective talk in classrooms, including the use of Talking Points and effective questioning. This chapter aims to give some background on effective dialogue of relevance to subsequent chapters, which will consider particular features of the interactive whiteboard in the context of dialogue

Finding knowledge – what is it to ‘know’ when we search?

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 fulfill, 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

The economics of Communist Party membership - The Curious case of rising numbers and wage premium during China’s transition

Why is it that, as the Chinese Communist Party has loosened its grip, abandoned its core beliefs, and marketized the economy, its membership has risen markedly along with the economic benefits of joining? We use three national household surveys, spanning eleven years, to answer this question with respect to labour market rewards in urban China. We conceptualize individual demand for Party membership as an investment in “political capital” that brings monetary rewards in terms of higher wages. This wage premium has risen with the growing wage differentials associated with the emergence of a labour market and the continuing value of political status in the semi-marketized transitional economy. However, a demand-side explanation does not explain the fact that the wage premium is higher for the personal characteristics that reduce the probability of membership. We develop an explanation in terms of a rationing of places and a scarcity value for members with those characteristics.China, Communist Party, labour market, economic transition, wages

Discourse-centric learning analytics: mapping the terrain

There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provide the opportunity to explore the ways in which: discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals make and share meaning together through their language use; and the particular types of language – from discipline specific, to argumentative and socio-emotional – associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence ‘good’ and ‘bad’ performance (the focus of many kinds of analytic), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers – the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g. learner), meso (e.g. school), and macro (e.g. governmental) levels

Discourse, computation and context – sociocultural DCLA revisited

This paper expands the sociocultural analysis of earlier discourse centric learning analytics (DCLA) to discuss the pedagogic functions of discourse, and the implications of these functions for DCLA. Given the importance of discourse for learning [13], and the potential of computers to (a) scaffold effective discourse and (b) give meaningful feedback on such discourse, it is important that DCLA are well theorised. Sociocultural theory emphasises context, and discourse “in action” in its analysis. If DCLA wishes to ground itself in such theory, work will need to be done to address these aspects of discourse in computational analysis. Given the potential of DCLA to provide support for educational talk – an important aspect of learning – research should be conducted to further develop DCLA approaches to such talk

A Discursive Approach to the Analysis of Epistemic Cognition

A core concern in learning is coming to understand the ways in which claims of knowledge are made. The epistemic cognition literature typically characterises this learning in terms of how learners cognitively conceptualise the source and nature of knowledge. Recent work has offered alternative accounts of epistemic cognition that recognise the discursive nature of the construct. These accounts are derived from analysis of the ways that learners talk about knowledge in tasks such as evaluating scientific claims from sources of varying qualities. In this paper we draw on this recent work to advance a novel approach to the analysis of discourse data in epistemic contexts. This approach is exemplified through its application to an existing dataset, demonstrating both the application of the approach and the particular kinds of discourse that learners engaged in. This discursive approach has the potential for broad application in the learning sciences’ treatment of epistemic cognition