Discourse-centric learning analytics

Liddo, Anna De; Shum, Simon Buckingham; Quinto, Ivana; Bachler, Michelle; Cannavacciuolo, Lorella

doi:10.1145/2090116.2090120

Cited by 80 publications

(46 citation statements)

References 12 publications

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“…Our example comes from work at the Open University, based around the Cohere social annotation and knowledge mapping tool (Buckingham Shum, 2008) and subsequent work on its potential for sociocultural discourse-centric learning analytics (De Liddo et al, 2011). Cohere is a web application for mapping ideas, concepts, and arguments that can be annotated directly onto source websites.…”

Section: Example: Discourse-centric Analytics For Epistemic Beliefsmentioning

confidence: 99%

Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space

2014

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Abstract:Learning Analytics is an emerging research field and design discipline that occupies the "middle space" between the learning sciences/educational research and the use of computational techniques to capture and analyze data (Suthers & 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, socio-cultural perspective are well placed to explore this construct using discourse-centric technologies. The examples provided throughout this paper, through emphasizing the consideration of intentional design issues in the middle space, underscore the "interpretative flexibility" (Hamilton & Feenberg, 2005) of new technologies, including analytics.

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Section: Example: Discourse-centric Analytics For Epistemic Beliefsmentioning

confidence: 99%

Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space

2014

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“…Students can then be advised to re-assess their participation, or simply to reflect on their position in the network. The text exchanged by students in discussion forums is also a valuable data source for more recent techniques known as discourse-centric analytics that seek to detect evidence of learning, and language usage patterns that are associated with positive academic outcomes (Ferguson and Buckingham Shum 2011;De Liddo et al 2011;Knight and Littleton 2015). The characterization of these discussions offers the possibility to provide highly detailed and potentially effective feedback for students to increase their performance.…”

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confidence: 99%

Data-Driven Personalization of Student Learning Support in Higher Education

Liu

Bartimote-Aufflick

Pardo

et al. 2017

Learning Analytics: Fundaments, Applications, and Trends

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Abstract. Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data -one that puts the relationship between teacher and student at the center. Our approach means that teachers' pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.Keywords: actionable intelligence, implementation, intelligence amplification, learning analytics, personalization, student engagement. Abbreviations EDMEducational data mining EWS Early warning system LA Learning analytics LMS Learning management system SRES Student Relationship Engagement System 2 Introduction The state of data-driven student supportThe rise in use of technology mediation in learning scenarios is providing unprecedented amounts of data about how educational institutions work and how students participate in learning experiences. At the same time, learning scenarios are becoming increasingly diverse and complex. The areas of educational data mining (EDM) and learning analytics (LA) have emerged to address the issue of how to use data to improve our understanding of learning, and enhance the overall quality of the learning experience for the student. Although EDM and LA researchers and practitioners maintain a similar focus (Baker and Siemens 2014), they differ in their approach to data generated in educational settings. Researchers in EDM frequently focus their analyses on the formulation or improvement of data mining algorithms designed to detect and predict important factors in a learning scenario. LA, on the other hand, focuses on how these algorithms can be deployed and integrated in learning designs, used by teachers, and provide tangible improvements for students. However, in their initial stages, both disciplines placed their emphasis mostly on how data can be collected and used by algorithms and not so much on how these data can then lead to actions that have a positive effect on students.Prior to the availability of massive amounts of data, the area...

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“…Computer environments may be seen as complementary to such dialogue, in particular where they formalize, through the user interface conceptual model, some of the systems through which exploratory and accountable dialogue are more likely to occur -the "ground rules" of each. It is thus that systems have been developed specifically to support particular types of formalized argumentation schema (Clark et al, 2007;De Liddo, Buckingham Shum, Quinto, Bachler, & Cannavacciuolo, 2011;Scheuer et al, 2010;Weinberger, Ertl, Fischer, & Mandl, 2005;Weinberger & Fischer, 2006). Again, we highlight the range of ways in which CSCL argumentation aligns with the table with some scripting aimed at individual learning gains (particularly around argumentation schema), and other methods aimed to aid in intersubjective meaning making.…”

Section: Structured Dialogue and Cscl Environmentsmentioning

confidence: 99%

Discourse Centric Learning Analytics: Mapping the Terrain

Knight

Littleton

2015

Learning Analytics

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ABSTRACT: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 provides 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 socioemotional -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 analytics), 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., government) levels.

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Discourse-centric learning analytics

Cited by 80 publications

References 12 publications

Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space

Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space

Data-Driven Personalization of Student Learning Support in Higher Education

Discourse Centric Learning Analytics: Mapping the Terrain

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