Download as PDF, PPTX
![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.”
26](https://image.slidesharecdn.com/sbskeynotelibrarydatacarpentry2016-160712005315/75/Who-are-you-and-makes-you-special-26-2048.jpg)
![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.](https://image.slidesharecdn.com/sbskeynotelibrarydatacarpentry2016-160712005315/75/Who-are-you-and-makes-you-special-43-2048.jpg)
![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.”
46](https://image.slidesharecdn.com/sbskeynotelibrarydatacarpentry2016-160712005315/75/Who-are-you-and-makes-you-special-46-2048.jpg)
![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.
47](https://image.slidesharecdn.com/sbskeynotelibrarydatacarpentry2016-160712005315/75/Who-are-you-and-makes-you-special-47-2048.jpg)
![http://governingalgorithms.org
In
an
increasingly
algorithmic
world
[…]
What,
then,
do
we
talk
about
when
we
talk
about
“governing
algorithms”?
49](https://image.slidesharecdn.com/sbskeynotelibrarydatacarpentry2016-160712005315/75/Who-are-you-and-makes-you-special-49-2048.jpg)
Uploaded bySimon Buckingham Shum
Who are you and makes you special?
Simon Buckingham Shum is a professor of learning informatics and director of the Connected Intelligence Centre at the University of Technology Sydney. He discusses how large scale data and analytics are changing society and education. His research focuses on developing human-centered approaches to data science, machine learning, and analytics to ensure they are used ethically and for the benefit of learning. He argues analytics require a critical perspective to avoid potential harms and should seek to understand issues beyond surface-level metrics.
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Who are you and makes you special?
- 1. Who are youand 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
- 2. 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)
- 3.
- 4. OUR CONTEXT 4 Large scaledata 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)
- 5. 2011 2011 Envisioning “the Data IntensiveUniversity” 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
- 6. UTS CONNECTED INTELLIGENCECENTRE 6 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
- 7.
- 8. “LibrAIrian”a University Librarystaff 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
- 10. What’s the differencebetween a LibrAIrian and a data technician?
- 11. Panel debate, LAK 2013 Withthanks to John Behrens (Pearson) hIp://simon.buckinghamshum.net/2013/03/lak13-‐edu-‐data-‐scien<sts-‐scarce-‐breed
- 12. Things that makeme crazy
- 13. “Looks cool. Whatdoes it mean?” “I don’t know”
- 14. “Looks great ata high level, how have you explored it and tested the assumptions?” “What assumptions?”
- 15. “What are therelevant background issues in this area?” “Ask the SME, I’m just the data person”
- 16. “Does it reallymake sense to get that result?” “I don’t know, it just came out that way”
- 17.
- 18. critical perspective algorithmic +scholarly + creative intelligence 18
- 19.
- 20. A DISTINCTIVE APPROACHTO 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
- 21. What’s the differencebetween a LibrAIrian and a learning analytics technician?
- 22. critical perspective learning analytics meansmany things to many people learning analytics are not neutral 22
- 23. It’s out ofthe labs and into products: every learning tool now has an “analytics dashboard” (a Google image search) 23
- 24.
- 25.
- 26. Intelligent tutoring forskills 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.” 26
- 27. 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
- 28. Spatial clustering algorithmto provoke reflection 28Eric 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
- 29. Co-located collaboration spaces Analysethe students’ activity traces for significant patterns Timely feedback for personal and team reflection
- 30. Co-located collaboration spaces …cannow be instrumented with sensors Voice Gesture Pen Touch
- 31. Co-location activity dashboards Multimodaldata 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
- 32. Visual analytics off-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.
- 33.
- 34. Posture analysis offieldwork 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
- 35. 1st International Workshopon 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://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg http://solaresearch.org/events/lak/lak13/dcla13
- 36. Highlighted sentences arecolour- 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
- 37. Reflective writing (Nursing) Applicationsfor 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
- 38. Educa<onal worldview 38 epistemology pedagogyassessment 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?
- 39. To go deeperinto 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)
- 40. What’s the differencebetween a LibrAIrian and a knowledge infrastructure scholar?
- 41. critical perspective knowledge infrastructures embodyvalues and assign power 41
- 42.
- 43. Framing future knowledge infrastructures http://knowledgeinfrastructures.org Thistoo, 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.
- 44. Framing future knowledge infrastructures http://knowledgeinfrastructures.org “Transformativeinfrastructures 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…”
- 45. Accounting tools arenot 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” 45
- 46. 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.” 46
- 47. 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. 47
- 48.
- 49. http://governingalgorithms.org In an increasingly algorithmic world […] What, then, do we talk about when we talk about “governing algorithms”? 49
- 50.
- 51. “LibrAIrian”a University Librarystaff 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 51
- 52.