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

Knight, Simon; Shum, Simon Buckingham; Littleton, Karen

doi:10.18608/jla.2014.12.3

Cited by 135 publications

(94 citation statements)

References 59 publications

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“…Furthermore, data literacy and use are highly relevant for both student teachers and practicing teachers in order to keep up with an increasing focus on formative e-assessment that uses educational data mining and learning analytics methods (e.g. Knight, Buckingham Shum & Littleton, 2014;Kickmeir-Rust et al, 2014;Vatrapu, Reimann, Hussain & Pantazos 2016).…”

Section: Discussionmentioning

confidence: 99%

Teacher Inquiry into Student Learning: - The TISL Heart Model and Method for use in Teachers’ Professional Development

Hansen¹,

Wasson²

2016

NJDL

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Researchers have recently been calling for new models of teacher education and professional development for the 21st century. Teacher inquiry, where the teacher's own practice is under investigation, can be seen both as a way to improve day-to-day teaching in the classroom and as professional development for teachers. As such, it should also have a role in teacher education. In this article, we present the iterative development of the TISL Heart, a theory-practice model and method of teacher inquiry into student learning, which has a particular emphasis on the use of student results generated in the information and technology-rich classroom. This article proposes that this practice-near model is particularly relevant for teacher education, as it draws upon existing practices in using student data at a progressive school that focuses on the use of technology to enhance student learning. The article concludes by discussing the implications for its role in teacher education, particularly related to data literacy and its use in teaching.

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Section: Discussionmentioning

confidence: 99%

Teacher Inquiry into Student Learning: - The TISL Heart Model and Method for use in Teachers’ Professional Development

Hansen¹,

Wasson²

2016

NJDL

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“…In relation to education specifically, there is burgeoning research on learning analytics and their potential to enhance provision for students, particularly in relation to higher education (Buckingham Shum 2014;Finn 2015;Gasevic, Dawson, and Siemens 2015). Within this realm, there are significant efforts to examine issues, such as how code and algorithms reflect and relate to specific understandings of pedagogy and assessment (Knight, Buckingham Shum, and Littleton 2014) and issues of student privacy (Prinsloo and Slade 2015). There are also attempts to develop greater transparency through the use of open source software and through participatory design approaches to the development of analytics.…”

Section: Standardisation Software Algorithms and Inscrutable Practicesmentioning

confidence: 99%

Digital analytics in professional work and learning

Edwards

Fenwick

2015

Studies in Continuing Education

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In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to the far-reaching and accelerating consequences of these forces, claiming that many professionals, researchers, policymakers and the public are just beginning to realise the enormous potentials and challenges these analytics are producing. Yet, outside of particular areas of research and practice, such as learning analytics, there has been little discussion of this to date in the broader education literature. This article aims to set out some key issues particularly relevant to the understandings of professional practice, knowledge and learning posed by the linkages of big data and software code. It begins by outlining definitions, forms and examples of these analytics, their potentialities and some of the hidden impact, and then presents issues for researchers and educators. It seeks to contribute to and extend debates taking place in certain quarters to a broader professional education and work audience. ARTICLE HISTORY

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“…Understanding this full cycle is crucial, and it is to this issue that we turn in the section "The Learning Analytics Lifecycle." While DCLA of various sorts may be productive, we should be aware of the interpretive flexibility of the tools we develop, and the pedagogic, assessment, and epistemological context of which they form a part (Knight et al, 2014). However, if we are able to operationalize some stable and general categories and patterns in discourse use, then we can model such patterns in classifiers, recognizing that such modelling involves a balance around acceptable levels of information loss (Rosé & Tovares, 2015).…”

Section: Enabling Sharing Of Ideas That Can Be Improved Togethermentioning

confidence: 99%

“…However, their use may also make commitments to particular pedagogic, assessment, and epistemological assumptions (Knight et al, 2014). While tools have particular affordances (for example, twitter replies, forum threading, knowledge-mapping connections), these tools can be used in many learning contexts, and their data analyzed for many purposes.…”

Section: What Systems Need To Be In Place For the Successful Deploymementioning

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

Cited by 135 publications

References 59 publications

Teacher Inquiry into Student Learning: - The TISL Heart Model and Method for use in Teachers’ Professional Development

Teacher Inquiry into Student Learning: - The TISL Heart Model and Method for use in Teachers’ Professional Development

Digital analytics in professional work and learning

Discourse Centric Learning Analytics: Mapping the Terrain

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