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Dr Simon Knight

Biography

My research focuses on how people think about knowledge and its representation. In philosophy of education my dissertation (Masters, UCL) looked at the epistemological side of assessment policy. My current work takes an empirical approach to student epistemic cognition - thinking about what we know, and how - particularly in information seeking and writing contexts. I've explored this by exploring the collaborative dialogue of small groups of children (MPhil, Cambridge) and undergraduates (PhD, Open) when using a collaborative browser addon (Coagmento, developed by Rutgers University).

I maintain a full CV and publication list on my personal website (see link below).

Professional

  • Section Editor of the Journal of Learning Analytics (hot spot section)
  • Chartered member of the British Psychological Society (CPsychol)
  • Member of Society for Text and Discourse
  • Member of Philosophy of Education Society of Great Britain
  • Member of Society of Learning Analytics Research
  • Qualified Secondary Teacher (I primarily taught A-level philosophy and psychology in the UK)
Lecturer, Connected Intelligence Centre
B.Sc (Intl Hons, Leeds), PGCE (UCL), MPhil (Cambridge), MA (UCL), Ph.D
 
Phone
+61 2 9514 8908

Research Interests

My current research explores student writing practices (including information seeking, reading, note taking, writing processes, and peer and self-assessment of writing). I'm particularly interested in the relationship of these practices to ways of thinking about knowledge and evaluation (epistemic cognition) and collaborative knowledge practices (including co-writing, formative feedback, collaborative information seeking, etc.). I take a systemic approach in considering the policy and practice context of writing and its analysis.
Can supervise: Yes

I'm interested in supporting students very broadly in learning analytics, and particularly in topics around epistemic cognition and student writing.

I coordinate the 'Data Science for Innovation' subject in our Masters in Data Science and Innovation, and teach on/coordinate the undergraduate quantitative literacy subject 'Arguments, Evidence, and Intuition'

Books

Knight, S. 2012, ORBIT Coursebook, ORBIT, Cambridge, UK.

Chapters

Knight, S. & Buckingham Shum, S. 2016, 'Theory and Learning Analytics' in The Handbook of Learning Analytics and Educational Data Mining.
Knight, S. & Littleton, K. 2015, 'Learning through Collaborative Information Seeking' in Hansen, P., Shah, C. & Klas, C. (eds), Collaborative Information Seeking: Best practices, New Domains, New Thoughts, Springer.
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This chapter discusses Collaborative Information Seeking (CIS) from an educational perspective. Our core claim is that CIS has the potential to bring together rich collaborative, and multimodal, contexts in which important learning processes may take place. We thus see CIS as more than just an activity with potential to 'speed up' information seeking, or contribute to effective division of labour. This claim is independent of the particular classroom subject, or the form of technological mediation; rather, the chapter provides a focus on some key considerations in collaborative learning that should be of interest to both educators and those interested in the 'benefits' of CIS. This chapter first outlines our broad educational interest in elements of CIS, connecting that to the focal points of CIS research. We go on to highlight the importance of dialogue as a tool for learning, before discussing the complexities of understanding 'success' in CIS tasks, and then specifically the role that dialogue has played so far in CIS research. We conclude with a call to researchers in both CIS and education to explore the nature of learning in CIS contexts, making use of a rich understanding of the importance of dialogue to create meaning together.
Newman, K., Knight, S., Elbeshausen, S. & Hansen, P. 2015, 'Situating CIS – The importance of Context in Collaborative Information Seeking' in Hansen, P., Shah, C. & Klas, C. (eds), Collaborative Information Seeking: Best practices, New Domains, New Thoughts, Springer.
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Collaborative Information Seeking (CIS) is common in many professional contexts. This chapter discusses CIS from four different perspectives – education, healthcare, science research and patent research. We first introduce the CIS context, focusing on Evans and Chi's proposed model of social search. We highlight the ways contextual factors relate to the search process, in particular noting the role of communication in CIS processes. The four example professional contexts are discussed with reference to the 'medium' of collaboration, the ways CIS is conducted, the tools used and physical setting of CIS, and the 'context' of CIS, the purposes for which an instance of CIS occurs in that discipline. We suggest that these contextual factors can be aligned with, but are additional to, the existing Evans and Chi model of social search, and that their addition in a 'pre- and post-model' extension could provide a shared framework for researching contextual features of CIS. In highlighting commonalities and contrasts across the disciplinary contexts we suggest that a developed model, and further research, is needed to understand the relationship between motivations in these different disciplines and the evaluation of CIS episodes, and the role of processes, particularly communication, in those episodes. In order to evaluate CIS in different disciplines future research should focus on the between, and within discipline differences in the purposes of CIS. Characteristics of success in different disciplinary contexts may relate both to the consideration of the collaborative context, and the information need; developing deeper understanding of this point.
Knight, S. & Littleton, K. 2015, 'Thinking, Interthinking, and Technological Tools' in Wegerif, R., Li, L. & Kaufman, J.C. (eds), The Routledge International Handbook of Research on Teaching Thinking, Routledge, pp. section-7(al).
The Routledge International Handbook of Research on Teaching Thinking provides a comprehensive guide to the current state of the art in research on teaching thinking. Across the world education for thinking is seen as the key to thriving in the Internet Age. The OECD suggest teaching thinking as key to growing a more successful economy, others claim it is needed for increased democratic engagement and for the well-being of individuals faced with the complexity of a globalised world. However there are questions about what we mean by 'thinking', how best to teach it and how best to access it. This book contains surveys and summaries of cutting edge research on every aspect of research on teaching thinking in a range of contexts. It is an essential guide for policy-makers, teachers and researchers who are interested in teaching thinking. The book will be divided into eight sections, each of which will have a one-page introduction from the editors. Theoretical perspectives on teaching thinking Approaches for teaching thinking Developing creative thinking Developing critical thinking and metacognition The assessment of thinking Teaching thinking in the context of STEM. Collaborative thinking and new technology Neuro-educational research on teaching thinking The field of research on teaching thinking has changed in the last twenty years. There has been a growing interest in the contexts of thinking, especially the social and emotional contexts of thinking. At the same time improvements in brain scanning technology have enabled more research on the neural basis of thinking and of teaching thinking. The advent of the Internet and related technologies has stimulated increased research into collaborative thinking mediated by technology. The growth in the economic power of the Asia Pacific Region has been coupled with a growth in research on teaching for thinking and creativity in Asia often explicitly motivated by an interest in education for those '21st Century Skills' de...
Knight, S. & Littleton, K. 2015, 'Thinking, interthinking, and technological tools' in Wegerif, R., Li, L. & Kaufman, J.C. (eds), The Routledge International Handbook of Research on Teaching Thinking, Routledge, pp. 467-478.
Knight, S. & Littleton, K. 2015, 'Learning through Collaborative Information Seeking' in Hansen, P., Shah, C. & Klas, C. (eds), Collaborative Information Seeking: Best practices, New Domains, New Thoughts, Springer, pp. 101-116.
View/Download from: Publisher's site
This chapter discusses Collaborative Information Seeking (CIS) from an educational perspective. Our core claim is that CIS has the potential to bring together rich collaborative, and multimodal, contexts in which important learning processes may take place. We thus see CIS as more than just an activity with potential to 'speed up' information seeking, or contribute to effective division of labour. This claim is independent of the particular classroom subject, or the form of technological mediation; rather, the chapter provides a focus on some key considerations in collaborative learning that should be of interest to both educators and those interested in the 'benefits' of CIS. This chapter first outlines our broad educational interest in elements of CIS, connecting that to the focal points of CIS research. We go on to highlight the importance of dialogue as a tool for learning, before discussing the complexities of understanding 'success' in CIS tasks, and then specifically the role that dialogue has played so far in CIS research. We conclude with a call to researchers in both CIS and education to explore the nature of learning in CIS contexts, making use of a rich understanding of the importance of dialogue to create meaning together.
Knight, S. 2014, 'Finding Knowledge: What Is It To 'Know' When We Search?' in Society of the Query Reader: Reflections on Web Search, Institute of Network Cultures, Amsterdam, The Netherlands.
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.
Knight, S. 2013, 'Appendix C7.1: Resources for Searching with the Internet' in Hennessy, S., Warwick, P., Brown, L., Rawlins, D. & Neale, C. (eds), Developing interactive teaching and learning using the IWB, Open University Press.
Knight, S. 2013, 'Creating a supportive environment for classroom dialogue' in Hennessy, S., Warwick, P., Brown, L., Rawlins, D. & Neale, C. (eds), Developing interactive teaching and learning using the IWB, Open University Press.

Conferences

Knight, S., Martinez-Maldonado, R., Gibson, A. & Shum, S.B. 2017, 'Towards mining sequences and dispersion of rhetorical moves in student written texts', ACM International Conference Proceeding Series, pp. 228-232.
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© 2017 ACM.There is an increasing interest in the analysis of both student's writing and the temporal aspects of learning data. The analysis of higher-level learning features in writing contexts requires analyses of data that could be characterised in terms of the sequences and processes of textual features present. This paper (1) discusses the extant literature on sequential and process analyses of writing; and, based on this and our own first-hand experience on sequential analysis, (2) proposes a number of approaches to both pre-process and analyse sequences in whole-texts. We illustrate how the approaches could be applied to examples drawn from our own datasets of 'rhetorical moves' in written texts, and the potential each approach holds for providing insight into that data. Work is in progress to apply this model to provide empirical insights. Although, similar sequence or process mining techniques have not yet been applied to student writing, techniques applied to event data could readily be operationalised to undercover patterns in texts.
Gibson, A., Shum, S.B., Aitken, A., Tsingos-Lucas, C., Sándor, Á. & Knight, S. 2017, 'Reflective writing analytics for actionable feedback', ACM International Conference Proceeding Series, pp. 153-162.
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© 2017 ACM.Reflective writing can provide a powerful way for students to integrate professional experience and academic learning. However, writing reflectively requires high quality actionable feedback, which is time-consuming to provide at scale. This paper reports progress on the design, implementation, and validation of a Reflective Writing Analytics platform to provide actionable feedback within a tertiary authentic assessment context. The contributions are: (1) a new conceptual framework for reflective writing; (2) a computational approach to modelling reflective writing, deriving analytics, and providing feedback; (3) the pedagogical and user experience rationale for platform design decisions; and (4) a pilot in a student learning context, with preliminary data on educator and student acceptance, and the extent to which we can evidence that the software provided actionable feedback for reflective writing.
Buckingham Shum, S., Knight, S., McNamara, D., Allen, L., Betik, D. & Crossley, S. 2016, 'Critical Perspectives on Writing Analytics'.
Chen, B., Wise, A.F., Knight, S. & Cheng, L. 2016, 'It's About Time: Putting Temporal Analytics into Practice: The 5th International Workshop on Temporality in Learning Data'.
Knight, S., Allen, L., Littleton, K., Rienties, B. & Tempelaar, D.T. 2016, 'Writing Analytics for Epistemic Features of Student Writing', Transforming Learning, Empowering Learners Conference Proceedings, International Conference of the Learning Sciences, International Society of the Learning Sciences, Inc. [ISLS], Singapore, pp. 194-201.
Abstract: Literacy, encompassing the ability to produce written outputs from the reading of multiple sources, is a key learning goal. Selecting information, and evaluating and integrating claims from potentially competing documents is a complex literacy task. Prior research exploring differing behaviours and their association to constructs such as epistemic cognition has used 'multiple document processing' (MDP) tasks. Using this model, 270 paired participants, wrote a review of a document. Reports were assessed using a rubric associated with features of complex literacy behaviours. This paper focuses on the conceptual and empirical associations between those rubric-marks and textual features of the reports on a set of natural language processing (NLP) indicators. Findings indicate the potential of NLP indicators for providing feedback regarding the writing of such outputs, demonstrating clear relationships both across rubric facets and between rubric facets and specific NLP indicators.
Knight, S. & Anderson, T. 2016, 'Action-oriented, Accountable, and inter(Active) Learning Analytics for Learners', ACM Press.
Chen, B., Wise, A.F., Knight, S. & Cheng, B.H. 2016, 'Putting temporal analytics into practice: The 5th international workshop on temporality in learning data', ACM International Conference Proceeding Series, pp. 488-489.
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© 2016 Copyright held by the owner/author(s).Interest in temporal analytics|analytics that probe tempo- ral aspects of learning so as to gain insights into the processes through which learning occurs|continues to grow. The re- lationships of temporal patterns to learning outcomes is a central area of interest. However, while the literature on temporal analyses is developing, there has been less consid- eration of the methods by which temporal analyses might be translated to actionable insights and thus, put into use in educational practice. Emerging temporal analysis tech- niques present both theoretical and practical challenges for producing and interpreting results. Synergetic actions are needed in order to support practitioners.
Martinez-Maldonado, R., Anderson, T., Shum, S.B. & Knight, S. 2016, 'Towards supporting awareness for content curation: The case of food literacy and behavioural change', CEUR Workshop Proceedings, pp. 42-46.
Copyright © 2016 for the individual papers by the papers' authors.This paper presents a theoretical grounding and a conceptual proposal aimed at providing support in the initial stages of sustained behavioural change. We explore the role that learning analytics and/or open learner models can have in supporting life-long learners to enhance their food literacy through a more informed curation process of relevant-content. This approach grounds on a behavioural change perspective that identifies i) knowledge, ii) attitudes, and iii) self-efficacy as key factors that will directly and indirectly affect future decisions and agency of life-long learners concerning their own health. The paper offers some possible avenues to start organising efforts towards the use of learning analytics to enhance awareness in terms of: knowledge curation, knowledge sharing and knowledge certainty. The paper aims at triggering discussion about the type of data and presentation mechanisms that may help life-long learners set a stronger basis for behavioural change in the subsequent stages.
Gibson, A., Knight, S., Aitken, A., Buckingham Shum, S., Ryan, P., Jarvis, W., Nikolova, N., Tsingos-Lucas, C., Parr, A., White, A. & Sutton, N. 2016, 'Using Writing Analytics For Formative Feedback', UTS Teaching and Learning Forum, University of Technology, Sydney.
Knight, S. & Littleton, K. 2015, 'Developing a multiple-document-processing performance assessment for epistemic literacy', Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, The 5th International Learning Analytics & Knowledge Conference (LAK15): Scaling Up: Big Data to Big Impact, ACM, Poughkeepsie, USA, pp. 241-245.
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The LAK15 theme 'shifts the focus from data to impact, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.
Knight, S., Wise, A.F., Chen, B. & Cheng, B.H. 2015, 'It's About Time: 4th International Workshop on Temporal Analyses of Learning Data', The 5th International Learning Analytics & Knowledge Conference (LAK15): Scaling Up: Big Data to Big Impact.
Interest in analyses that probe the temporal aspects of learning continues to grow. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these are associated with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. Learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges as it is already positioned in the ''middle space' where learning and analytic concerns meet (Suthers & Verbert, 2013, p 1). This workshop, the fourth in a series on temporal analysis of learning, provides a focal point for analytics researchers to consider issues around and approaches to temporality in learning analytics.
Knight, S. & Mitsui, M. 2015, 'Temporal analysis in epistemic SCIS tasks', ECol 2015 - Proceedings of the 2015 Workshop on Evaluation on Collaborative Information Retrieval and Seeking, co-located with CIKM 2015, 2015 Workshop on Evaluation on Collaborative Information Retrieval and Seeking, co-located with CIKM 2015, ACM, Melbourne, Australia, pp. 15-16.
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Copyright is held by the owner/author(s). Temporal considerations are core to understanding both learning, and information seeking processes and outcomes. This claim is true of both individual, and collaboratively based work. Indeed, limited analysis has been conducted in both the learning and information sciences to describe temporal features of SCIS. This paper discusses some of this work, which provides the background for ongoing analysis (to be presented at the workshop) of temporal factors in a designed epistemic SCIS task.
Knight, S. 2015, 'Learning indicators in SCIS tasks', ECol 2015 - Proceedings of the 2015 Workshop on Evaluation on Collaborative Information Retrieval and Seeking, co-located with CIKM 2015, 2015 Workshop on Evaluation on Collaborative Information Retrieval and Seeking, ACM, Melbourne, Australia, pp. 11-13.
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Copyright is held by the owner/author(s). Evaluation of user success, and systems to support that success, in information seeking tasks is complex. The addition of social or/and collaborative elements to task and system design adds an additional layer of complexity to such evaluation. This short paper highlights a number of simple metrics that have been used by information and learning science researchers to explore SCIS in the context of searching to learn.
Knight, S., Arastoopour, G., Williamson Shaffer, D., Buckingham Shum, S. & Littleton, K. 2013, 'Epistemic Networks for Epistemic Commitments', International Society of the Learning Sciences.
Knight, S., Wise, A., Arastoopour, G., Shaffer, D.W., Buckingham Shum, S. & Kirschner, P.A. 2014, 'Learning analytics for learning and becoming in practice', International Conference of the Learning Sciences.
Arastoopour, G., Shum, S.B., Collier, W., Kirschner, P.A., Wise, A.F., Knight, S. & Shaffer, D.W. 2014, 'Analytics for learning and becoming in practice', Proceedings of International Conference of the Learning Sciences, ICLS, pp. 1680-1683.
© ISLS. Learning Analytics sits at the intersection of the learning sciences and computational data capture and analysis. Analytics should be grounded in the existing literature with a view to data 'geology' or 'archeology' over 'mining'. This workshop explores how analytics may extend the common notion of activity trace data from learning processes to encompass learning practices, with a working distinction for discussion as (1) process: a series of related actions engaged in as part of learning activities; and (2) practice: a repertoire of processes organised around particular foci recognised within a social group. The workshop intersperses attendee presentations and demonstrations with relevant theme-based discussions.
Knight, S., Buckingham Shum, S. & Littleton, K. 2013, 'Epistemology, Pedagogy, Assessment and Learning Analytics', Proc. 3rd International Conference on Learning Analytics & Knowledge, pp. 75-84.

Journal articles

Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á. & Wang, X. 2017, 'Academic Writing Analytics for Civil Law: Participatory Design Through Academic and Student Engagement', International Journal of Artificial Intelligence in Education.
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Research into the teaching and assessment of student writing shows that many students find academic writing a challenge to learn, with legal writing no exception. Improving the availability and quality of timely formative feedback is an important aim. However, the time-consuming nature of assessing writing makes it impractical for instructors to provide rapid, detailed feedback on hundreds of draft texts which might be improved prior to submission. This paper describes the design of a natural language processing (NLP) tool to provide such support. We report progress in the development of a web application called AWA (Academic Writing Analytics), which has been piloted in a Civil Law degree. We describe: the underlying NLP platform and the participatory design process through which the law academic and analytics team tested and refined an existing rhetorical parser for the discipline; the user interface design and evaluation process; and feedback from students, which was broadly positive, but also identifies important issues to address. We discuss how our approach is positioned in relation to concerns regarding automated essay grading, and ways in which AWA might provide more actionable feedback to students. We conclude by considering how this design process addresses the challenge of making explicit to learners and educators the underlying mode of action in analytic devices such as our rhetorical parser, which we term algorithmic accountability.
Knight, S. & Mercer, N. 2017, 'Collaborative epistemic discourse in classroom information-seeking tasks', Technology, Pedagogy and Education, vol. 26, no. 1, pp. 33-50.
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© 2016 Association for Information Technology in Teacher Education.The authors discuss the relationship between information seeking and epistemic beliefs–beliefs about the source, structure, complexity and stability of knowledge–in the context of collaborative information-seeking discourses. They further suggest that both information seeking, and epistemic cognition research agendas, have suffered from a lack of attention to how information seeking as a collaborative activity is mediated by talk between partners–an area they seek to address in this article. A small-scale observational study using sociocultural discourse analysis was conducted with eight 11-year-old pupils who carried out search engine tasks in small groups. Qualitative and quantitative analysis were performed on their discussions using sociocultural discourse analytic techniques. Extracts of the dialogue are reported, informed by concordance analysis and quantitative coding of dialogue duration. The authors find that: (1) discourse which could be characterised as 'epistemic' is identifiable in student talk; (2) it is possible to identify talk which is more or less productive; and (3) epistemic talk is associated with positive learning outcomes.
Knight, S. & Littleton, K. 2017, 'Socialising Epistemic Cognition', Educational Research Review.
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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.
Knight, S. & Mercer, N. 2016, 'The role of collaborative, epistemic discourse in classroom information seeking tasks', Technology, Pedagogy and Education.
We discuss the relationship between information seeking, and epistemic beliefs – beliefs about the source, structure, complexity, and stability of knowledge – in the context of collaborative information seeking discourses. We further suggest that both information seeking, and epistemic cognition research agendas have suffered from a lack of attention to how information seeking as a collaborative activity is mediated by talk between partners – an area we seek to address in this paper. A small-scale observational study using sociocultural discourse analysis was conducted with eight eleven year old pupils who carried out search engine tasks in small groups. Qualitative and quantitative analysis were performed on their discussions using sociocultural discourse analytic techniques. Extracts of the dialogue are reported, informed by concordance analysis and quantitative coding of dialogue duration. We find that 1) discourse which could be characterised as 'epistemic' is identifiable in student talk, 2) that it is possible to identify talk which is more or less productive, and 3) that epistemic talk is associated with positive learning outcomes.
Knight, S. & Littleton, K. 2016, 'Dialogue as Data in Learning Analytics for Productive Educational Dialogue', Journal of Learning Analytics, vol. 2, no. 3, pp. 111-143.
Knight, S. & Littleton, K. 2015, 'Discourse-Centric Learning Analytics: Mapping the Terrain', Journal of Learning Analytics.
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.
Knight, S. & Mercer, N. 2015, 'The role of exploratory talk in classroom search engine tasks', Technology, Pedagogy and Education, vol. 24, no. 3, pp. 303-319.
Haler, B., Hennessy, S., Knight, S. & Connolly, T. 2014, 'Developing an Open Resource Bank for Interactive Teaching of STEM: Perspectives of school teachers and teacher educators', Journal of Interactive Media in Education.
Knight, S., Buckingham Shum, S. & Littleton, K. 2014, 'Epistemology, assessment, pedagogy: where learning meets analytics in the middle space', Journal of Learning Analytics, vol. 1, no. 2.
Knight, S. 2011, 'AFL FOR INCLUSIVE DIFFERENTIATED LEARNING', Practical Research in Education, vol. 44, pp. 57-63.
Knight, S. 2011, 'Anger or Fairness in Ultimatum Game Rejections?', Journal of European Psychology Students.
Knight, S. & Zupan, Z. 2011, 'Besplatna tehnologija u psihološkim istraživanjima. (Free technology in psychological research)', Psihološka istraživanja, vol. XIV, no. 1, pp. 99-106.
Knight, S. & Vainre, M. 2011, 'Career Aspirations and Self-Efficacy of European Psychology Students', Psychology Teaching Review, vol. 17 (Themed Issue - Psychology around the world), no. 2, pp. 47-60.
Knight, S. 2011, 'Using the ultimatum game to teach economic theories of relationship maintenance to A-level students', Psychology Teaching Review, vol. 17, no. 1, pp. 46-49.
Knight, S. 2011, 'So you want to do (free) research?', Psychologist, vol. 24, no. 11, pp. 828-830.
Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á. & Wang, X., 'Designing Academic Writing Analytics for Civil Law Student Self-Assessment', International Journal of Artificial Intelligence in Education.
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Other

Buckingham Shum, S., Knight, S., Gibson, A.P., Ryan, P. & Aitken, A. 2016, 'Writing Analytics to Improve Formative Feedback'.
Webinar for Transforming Assessment
Knight, S. 2012, 'Finding Knowledge - The Role of Dialogue in Collaborative Information Retrieval in the Classroom'.
Knight, S. 2011, 'Knowing my extensions: THE IMPLICATIONS OF EXTENDED MIND FOR OUR CONCEPTION OF KNOWLEDGE AND ITS ASSESSMENT – DO I 'KNOW' MY EXTENSIONS?'.
Knight, S., 'Developing Learning Analytics for Epistemic Commitments in a Collaborative Information Seeking Environment'.

Reports

Buckingham Shum, S. & Knight, S. Educational Technology Action Group 2014, Educational Technology Action Group cluster 2a (Students with sight & control of their own complex learning 'big data) consultation.
Knight, S., Maggs, M., Poulter, M., Matthews, C. & Sant, T. 2014, Wikimedia UK response to House of Lords Digital Skills Committee call for evidence.
  • In the AWA project we collaborate with Xerox Research in Europe (XRCE)
  • Work from my PhD is in collaboration with:
    • Chirag Shah and Matthew Mitsui at Rutgers University InfoSeeking Lab http://www.infoseeking.org/
    • Dirk Tempelaar at Maastricht University,
    • Bart Rienties and Karen Littleton at the Open University, UK
  • We have active collaborations with the SOLET (Science of Learning and Educational Technology) lab at Arizona State University
  • We collaborate with a number of Australian universities