Who are you and makes you special?
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Transcript of Who are you and makes you special?
Who are you and makes you special?
Simon Buckingham Shum Professor of Learning Informatics Director, UTS Connected Intelligence Centre @sbuckshum • Simon.BuckinghamShum.net
utscic.edu.au
Keynote, Library Data Carpentry 2016, Sydney http://librarydatacarpentry.github.io
Learning Technology
KMi, Open U.
AI & Argumenta<on
Learning Disposi<ons
Human-‐Centred Informa<cs
Learning Analy<cs
Seman<c Scholarly Publishing
Dialogue / Issue / Argument Visualisa<on
Introducing my quantified
background
(at least, in Nov. 2013 courtesy LinkedIn Labs)
OUR CONTEXT
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Large scale data and analytics are pervading societal life
Data and Algorithms have deep societal implications – good and bad – demanding informed debate
Implications for the future workforce…
How universities teach, research, operate — and are assessed…
How to equip graduates for “the age of complexity” (Stephen Hawking)
2011 2011
Envisioning “the Data Intensive University”
UTS-wide Forum to consider the profound implications of
the data revolution
Followed by UTS-wide consultation, strategy devpt,
and launch of CIC
UTS STRATEGIC CONVERSATION AROUND ANALYTICS
UTS CONNECTED INTELLIGENCE CENTRE
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CIC catalyses the use of data and analytics among UTS students, educators, researchers and leaders
We teach human-centred data science • design analytics tools for UTS • evaluate these • disseminate internally and globally
We aim to shape critical debate on big data in education, and societal learning
“LibrAIrian” a University Library staff member who advises
students, educators and researchers on the uses and abuses of
AI, Data Science and Human-Centered Computing for learning, knowledge and innovation
Panel debate, LAK 2013 With thanks to John Behrens (Pearson)
hIp://simon.buckinghamshum.net/2013/03/lak13-‐edu-‐data-‐scien<sts-‐scarce-‐breed
“Looks great at a high level, how have you explored it and tested the assumptions?” “What assumptions?”
A DISTINCTIVE APPROACH TO DATA SCIENCE
utscic.edu.au
machine learning • statistics • data curation • ethics • user experience • information science visualization • narrative • social computing • learning analytics • project management project-based learning • authentic assessment regular, meaningful employer engagement
critical perspective learning analytics
means many things to many people
learning analytics are not neutral 22
It’s out of the labs and into products: every learning tool now has an “analytics dashboard” (a Google image search)
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https://guides.instructure.com/m/4152/l/66789-what-are-course-analytics
Summary statistics in the LMS (Canvas)
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Intelligent tutoring for skills mastery (CMU)
Lovett M, Meyer O and Thille C. (2008) The Open Learning Initiative: Measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education 14. http://jime.open.ac.uk/article/2008-14/352
“In this study, results showed that OLI-Statistics students [blended learning] learned a full semester’s worth of material in half as much time and performed as well or better than students learning from traditional instruction over a full semester.”
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Purdue University Signals: real time traffic-lights for students based on predictive model
27 Campbell et al (2007). Academic Analytics: A New Tool for a New Era, EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40–57. http://bit.ly/lmxG2x
Validate a statistical model from: • ACT or SAT score • Overall grade-point average • CMS usage composite • CMS assessment composite • CMS assignment composite • CMS calendar composite
Predicted 66%-80% of struggling students who needed help
Spatial clustering algorithm to provoke reflection
28 Eric Coopey, R. Benjamin Shapiro, and Ethan Danahy. 2014. Collaborative spatial classification. In Proceedings of the 4th International Conference on Learning Analytics & Knowledge (LAK '14). ACM, New York, NY, USA, 138-142. DOI= http://dx.doi.org/10.1145/2567574.2567611
Co-located collaboration spaces Analyse the students’ activity traces for significant patterns Timely feedback for personal and team reflection
Co-location activity dashboards Multimodal data fusion and analysis… …to deliver visual analytics for reflection
e.g. this dashboard shows team member participation on different modalities
Applications for researchers working on high performance teams; group dynamics?
R. Martinez, K. Yacef, J. Kay, and B. Schwendimann. An interactive teacher’s dashboard for monitoring multiple groups in a multi-tabletop learning environment. Proceedings of Intelligent Tutoring Systems, pages 482-492. Springer, 2012.
Voice
Gesture
Pen
Touch
Visual analytics of f-f teamwork
R. Martinez, K. Yacef, J. Kay, and B. Schwendimann. An interactive teacher’s dashboard for monitoring multiple groups in a multi-tabletop learning environment. Proceedings of Intelligent Tutoring Systems, pages 482-492. Springer, 2012.
Posture analysis of fieldwork students
34 Masaya Okada and Masahiro Tada. 2014. Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environment. Proceedings 4th International Conference on Learning Analytics and Knowledge (LAK '14). ACM, New York, NY, USA, 1-10. DOI= http://dx.doi.org/10.1145/2567574.2567579
1st International Workshop on Discourse-Centric Learning Analytics
analytics that look beneath the surface, and quantify linguistic proxies for ‘deeper learning’
Beyond number / size / frequency of posts; ‘hottest thread’
http://ww
w.glennsasscer.com
/wordpress/w
p-content/uploads/2011/10/iceberg.jpg
http://solaresearch.org/events/lak/lak13/dcla13
Highlighted sentences are colour-coded according to their broad type
Sentences have Function Keys signalling where an academic rhetorical move has been
recognised (e.g. a claim of Novelty )
AWA: Academic Writing Analytics ANALYTICAL writing
https://utscic.edu.au/tools/awa
Reflective writing (Nursing)
Applications for researchers working with text corpora, e.g. interview transcripts; literature
analysis; scenario planning?
Buckingham Shum, S., Ágnes Sándor, Rosalie Goldsmith, Xiaolong Wang, Randall Bass and Mindy McWilliams (2016, In Press). Reflecting on Reflective Writing Analytics: Assessment Challenges and Iterative Evaluation of a Prototype Tool. 6th International Learning Analytics & Knowledge Conference (LAK16). Edinburgh, UK. ACM Press. http://dx.doi.org/10.1145/2883851.2883955 Preprint: http://bit.ly/LAK16paper
Educa<onal worldview
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epistemology
pedagogy assessment
Knight, S., Buckingham Shum, S. and Littleton, K. (2014). Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space. Journal of Learning Analytics, 1, (2), pp.23-47. http://epress.lib.uts.edu.au/journals/index.php/JLA/article/download/3538/4156 Knight, S. and Buckingham Shum, S. (In Press). Theory & Learning Analytics. Handbook of Learning Analytics & Educational Data Mining.
the middle
space of learning analytics
What epistemological assumptions are shaping the assessment regime, and hence the pedagogy? What questions are analytics used to help answer?
To go deeper into analytics for “21st century competencies”
39 hIp://simon.buckinghamshum.net/2015/05/cfp-‐learning-‐analy<cs-‐for-‐c21-‐competencies
Contributions are invited to this special issue: • Analytics for higher order competencies such as critical thinking,
curiosity, resilience, creativity, collaboration, sensemaking, self-regulation, reflection/meta-cognition, transdisciplinary thinking, or skilful improvisation
• Theoretical arguments around the opportunities, or indeed the limits, for analytics in illuminating particular competencies
• Principles and methodologies for combining complementary analytical approaches, including reflections on conventional educational assessment instruments, and computational approaches
• Methodologies for validating analytics • Analytics for learning dispositions/mindsets/“non-cognitive” factors
known to shape readiness to engage in learning • Analytics for different kinds of authentic assessment and inquiry-based
learning • Technological challenges and opportunities for lifelong, life-wide
learning analytics extending beyond formal educational contexts • Arguments regarding whether analytics could effect a shift in the
assessment regimes, and associated pedagogies and epistemologies, promoted by conventional education policy
• Analysis of the systemic organisational adoption issues for such analytics
• Visualisation design for different user groups, in particular, to promote increasing learner self-awareness and capacity to take responsibility for one’s learning
Next Special Issue (due July 2016)
Framing future knowledge infrastructures
http://knowledgeinfrastructures.org
This too, however, is not a neutral feature. As knowledge infrastructures shape, generate and distribute knowledge, they do so differentially, often in ways that encode and reinforce existing interests and relations of power. […] At scale, the effect of these choices may be an aggregate imbalance in the structure and distribution of our knowledge.
Framing future knowledge infrastructures
http://knowledgeinfrastructures.org
“Transformative infrastructures cannot be merely technical; they must engage fundamental changes in our social institutions, practices, norms and beliefs as well. For that reason, many scholars have dropped the dualistic vocabulary of “technical” and “social” altogether as anything other than a first order approximation, replacing those terms with concepts such as collectives (Latour 2005), assemblages (Ong & Collier 2005), or configurations (Suchman 2007…”
Accounting tools are not neutral
Du Gay, P. and Pryke, M. (2002) Cultural Economy: Cultural Analysis and Commercial Life. Sage, London. pp. 12-13
“accounting tools...do not simply aid the measurement of economic activity, they shape the reality they measure”
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Bowker, G. C. and Star, L. S. (1999). Sorting Things Out: Classification and Its Consequences. MIT Press, Cambridge, MA, pp. 277, 278, 281
“Classification systems provide both a warrant and a tool for forgetting [...] what to forget and how to forget it [...] The argument comes down to asking not only what gets coded in but what gets coded out of a given scheme.”
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Selwyn, N. (2014). Data entry: towards the critical study of digital data and education. Learning, Media and Technology. http://dx.doi.org/10.1080/17439884.2014.921628
“observing, measuring, describing, categorising, classifying, sorting, ordering and ranking). […] these processes of meaning-making are never
wholly neutral, objective and ‘automated’ but are fraught with problems and compromises, biases and omissions.
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http://governingalgorithms.org
In an increasingly algorithmic world […] What, then, do we talk about when we talk about “governing algorithms”?
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To learn more…
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hIp://simon.buckinghamshum.net/2016/03/algorithmic-‐accountability-‐for-‐learning-‐analy<cs
“LibrAIrian” a University Library staff member who advises
students, educators and researchers on the uses and abuses of
AI, Data Science and Human-Centered Computing for learning, knowledge and innovation
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