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2021_07_01 «Learning Informatics as Inspiration for Learning Analytics».

«Learning Informatics as Inspiration for Learning Analytics». Simon Buckingham-Shum

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Simon Buckingham Shum
Professor of Learning Informatics
Director, Connected Intelligence Centre
Learning Informatics as Inspiration
for Learning Analytics
cic.uts.edu.au • Simon.BuckinghamShum.net • @sbuckshum
eMadrid 2021 • emadridnet.org
overview
learning analytics (+ human-centred design)
informatics
learning informatics
4 informatics principles for human-centred LA
what this looks like in action
3
Learning Analytics
4
What is Learning Analytics?
Learning
student engagement
teaching practice
curriculum design
instructional design
pedagogy
assessment
epistemology
…
Analytics
data
statistics
classification
machine learning
text processing
visualisation
predictive models
…
this is not a straightforward dialogue!
5
A key circle is missing…
Human
Factors
stakeholder involvement
participatory design cycles
user interface design
privacy and ethics
end-user evaluation
organisational strategy
staff training
6
Learning Analytics: A Human-Centred Design Discipline
Learning Analytics
Human
Factors

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2021_07_01 «Learning Informatics as Inspiration for Learning Analytics».

  • 1. Simon Buckingham Shum Professor of Learning Informatics Director, Connected Intelligence Centre Learning Informatics as Inspiration for Learning Analytics cic.uts.edu.au • Simon.BuckinghamShum.net • @sbuckshum eMadrid 2021 • emadridnet.org
  • 2. overview learning analytics (+ human-centred design) informatics learning informatics 4 informatics principles for human-centred LA what this looks like in action
  • 4. 4 What is Learning Analytics? Learning student engagement teaching practice curriculum design instructional design pedagogy assessment epistemology … Analytics data statistics classification machine learning text processing visualisation predictive models … this is not a straightforward dialogue!
  • 5. 5 A key circle is missing… Human Factors stakeholder involvement participatory design cycles user interface design privacy and ethics end-user evaluation organisational strategy staff training
  • 6. 6 Learning Analytics: A Human-Centred Design Discipline Learning Analytics Human Factors
  • 7. 7 Learning Analytics: A Human-Centred Design Discipline Learning Analytics Human Factors https://www.emadridnet.org/index.php/en/28-eventos-y-seminarios/1214-human-centred-design-and-learning-analytics-where-are-we-at-and-where-should-we-go-next
  • 9. 9 informatics We need an overarching lens for designing effective, usable, ethical learning technology
  • 10. 10 Kristen Nygaard August 27, 1926 – August 10, 2002 Internationally acknowledged as the co-inventor with Ole-Johan Dahl of object-oriented programming and the programming language Simula. He also was a pioneer of participatory design and the “Scandinavian school of systems development”. http://kristennygaard.no
  • 11. 11 Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress, INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
  • 12. 12 “Informatics is the science that has as its domain information processes and related phenomena in artifacts, society and nature”. Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress, INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
  • 13. 13 No management hand-waving about valuing “user participation” Let’s get specific: who are we talking about? Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress, INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
  • 14. 14 How will such users get to “participate”? Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress, INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
  • 15. “Informatics is a distinct scientific discipline, characterised by its own concepts, methods, body of knowledge and open issues. It covers the foundations of computational structures, processes, artefacts and systems; and their software designs, their applications, and their impact on society” https://www.informatics-europe.org This laid the foundations for Informatics today…
  • 16. “Informatics concerns itself with the study of living, working and building in a digital world. Wherever technology touches people, it must be designed with ultimate care. This requires mastery of technological knowhow and a deep appreciation of the social, cultural and organizational forces at work.” https://www.informatics.uci.edu/explore/chairs-welcome This laid the foundations for Informatics today…
  • 17. 17 learning informatics Note that the terms Education(al) Informatics have been proposed, and some definitions of this are similar, albeit limited to educational institutions — I prefer a broader focus on lifelong/lifewide learning. Levy, P., Ford, N., Foster, J., Madden, A., Miller, D., Nunes, M. B., et al. (2003). Educational informatics: An emerging research agenda. Journal of Information Science, 29(4), 298-210. [reprint] Collins, J.W., and Weiner, S.A..(2010). Proposal for the creation of a subdiscipline: Education informatics. Teachers College Record 112, no. 10: 2523–2536. [reprint]
  • 18. 18 learning informatics How does this lens translate into deploying Analytics/AI for learning? Design for educator and learner agency Design socio-technical systems Design for imperfect computational models System Integrity > algorithmic accountability
  • 20. Framework @UTS for educators to co-design Analytics/AI à augment teaching practice PhD by Antonette Shibani: http://simon.buckinghamshum.net/2019/11/congratulations-dr-antonette-shibani Shibani, A., Knight, S. and Buckingham Shum, S. (2019). Contextualizable Learning Analytics Design: A Generic Model, and Writing Analytics Evaluations. Proc. 9th International Conference on Learning Analytics & Knowledge (LAK19). ACM Press, NY, pp. 210-219. DOI: https://doi.org/10.1145/3303772.3303785. Eprint: https://tinyurl.com/lak19clad Student Task Design Feedback & User Interface Features in the Data Educators Analytics/AI designers Assessment
  • 21. Acknowledgement: https://www.freevector.com/jigsaw-puzzle# Educator /Student Resources Learning Design Evaluation Analytics Capability Bundling analytics with educator resources Integrating Writing Activities with Writing Analytics Higher Education Text Analytics consortium: http://heta.io/resources UTS Orientation Portal: https://uts.edu.au/acawriter
  • 22. Automated feedback systems should enhance educators’ and students’ feedback literacy DAFFI 2020: Designing Automated Feedback for Impact 8-9 Sept. 2020 • bit.ly/daffi2020 https://cic.uts.edu.au/ensuring-automated-feedback-is-pedagogically-sound-daffi2020
  • 23. Raising data/learning analytics literacy (staff and students) UTS Open: https://open.uts.edu.au
  • 24. Disseminating innovations in Learning Analytics to the UTS community Briefings • Hands-on Training • Teaching into degree programs CIC Events archive: https://cic.uts.edu.au/events Raising data/learning analytics literacy (staff and students)
  • 25. 25 Design for educator and learner agency PhDs by Carlos Alvarez-Prieto, Vanessa Echeverria, Antonette Shibani http://simon.buckinghamshum.net/2020/09/congratulations-dr-carlos-prieto-alvarez http://simon.buckinghamshum.net/2020/05/congratulations-dr-vanessa-echeverria http://simon.buckinghamshum.net/2019/11/congratulations-dr-antonette-shibani Learning Informatics Principle 2
  • 26. Multimodal student data from simulations
  • 27. Who did what, when in a nursing simulation? Team Timeline for evidence-based debriefings Patient’s state changes Nurses 1-3 Uses a device Administers medication Personalised feedback on high performance teamwork Echeverria, V., Martinez-Maldonado, R. and Buckingham Shum, S. (2019). Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. In Proceedings of ACM Conference on Human Factors in Computing (CHI’19). ACM: NY. Paper 39, pp. 1-16. https://doi.org/10.1145/3290605.3300269 Open Access Eprint: http://bit.ly/chi19utscic
  • 28. Co-design techniques to engage students and staff in designing multimodal learning analytics Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2018). Mapping Learner-Data Journeys: Evolution of a Visual Co-design Tool. Proceedings of the 30th Australian Conference on Computer-Human Interaction (OzCHI’18), Melbourne, Australia, Dec. 2018, ACM, New York, NY, USA, pp. 205–214. DOI: https://doi.org/10.1145/3292147.3292168
  • 29. 29 Toolkit available http://ladeck.utscic.edu.au LA-DECK: card-based co-design tool for LA Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2020). LA-DECK: A Card-Based Learning Analytics Co-Design Tool. Proc.10th International Conference on Learning Analytics and Knowledge, Frankfurt, March 2020, ACM. 10 pages. DOI: https://doi.org/10.1145/3375462.3375476
  • 30. 30 LA-DECK: card-based co-design tool for LA Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2020). LA-DECK: A Card-Based Learning Analytics Co-Design Tool. Proc.10th International Conference on Learning Analytics and Knowledge, Frankfurt, March 2020, ACM. 10 pages. DOI: https://doi.org/10.1145/3375462.3375476
  • 31. Automated formative feedback on reflective writing Knight, S., Shibani, A., Abel, S., Gibson, A., Ryan, P., Sutton, N., Wight, R., Lucas, C., Sándor, Á., Kitto, K., Liu, M., Mogarkar, R. & Buckingham Shum, S. (2020). AcaWriter: A learning analytics tool for formative feedback on academic writing. Journal of Writing Research, 12, (1), 141-186. https://doi.org/10.17239/jowr-2020.12.01.06
  • 32. Participatory prototyping with educators to build trust in the NLP http://heta.io/how-can-writing-analytics-researchers-rapidly-codesign-feedback-with-educators Learning Analytics researchers work with academics (3 hour workshop) Goal: calibrate the parser detecting affect in reflective writing, working through sample texts Rapid prototyping with a Python notebook, then integrated into full application for further testing
  • 33. Design for imperfect computational models Kirsty Kitto, Simon Buckingham Shum, and Andrew Gibson. (2018). Embracing Imperfection in Learning Analytics. In Proceedings LAK18: International Conference on Learning Analytics and Knowledge, March 5–9, 2018, Sydney, NSW, Australia, pp.451-460. (ACM, New York, NY, USA). https://doi.org/10.1145/3170358.3170413 Learning Informatics Principle 3 (Ergonomics principle: wise allocation of function between human and machine)
  • 34. The Navajo rug “In a Navajo rug there is always an imperfection woven into the corner. And interestingly enough, it’s where “the Spirit moves in and out of the rug.” The pattern is perfect and then there’s one part of it that clearly looks like a mistake . . . Perfection is not the elimination of imperfection. […] Perfection, rather, is the ability to incorporate imperfection! […] You either incorporate imperfection, or you fall into denial.” http://exhibitions.kelsey.lsa.umich.edu/less-than-perfect/navajo.php Richard Rohr (2011). Breathing Under Water: Spirituality and the Twelve Steps. Cincinnati, OH: Franciscan Media See also: https://medium.com/bedolabs/success-through-imperfection-c3ef21cb32ed
  • 35. We must equip graduates with the distinctive qualities that will keep them in jobs that won’t be automated… …but if we want to use LA in such teaching and learning, tracking/assessing such competencies will be imperfect
  • 36. Machines will never fully model our higher order capabilities – but that can be an educational feature not a bug Reflection and metacognition are among the highest order outcomes that we aspire to cultivate in learners — the desire and skills to observe one’s thoughts, emotions and actions, and glean insights Learning Analytics in such contexts will in principle have a high degree of imperfection
  • 37. Embodied, skilled performance an important part of the learning experience is physically embodied (e.g. inspecting a forest; a social services risk assessment) Wicked problems (Horst Rittel) problems with no correct answer, and no stopping rules — even the definition of the problem is contested Socially complex performance scenarios where the outcome is emergent in nature, a function of many drivers that result in unpredictable and/or unique outcomes (e.g. a social worker client interview; conflict resolution) Learning Analytics in such contexts will in principle have a high degree of imperfection Machines will never fully model our higher order capabilities – but that can be an educational feature not a bug Can we design for this?
  • 38. Imperfect Learning Analytics à cognitive dissonance 38 Kirsty Kitto, Simon Buckingham Shum, and Andrew Gibson. (2018). Embracing Imperfection in Learning Analytics. In Proceedings LAK18: International Conference on Learning Analytics and Knowledge, March 5–9, 2018, Sydney, NSW, Australia, pp.451-460. (ACM, New York, NY, USA). https://doi.org/10.1145/3170358.3170413 “…as D’Mello and Graesser [15] demonstrate, it is when the student experiences dissonance because the analytics fail to match their expectations that they are likely to reflect on why they think the machine is wrong. We believe that this form of critical questioning is more likely to happen if the student has been given an underlying reason to be a little distrustful of the classifier.”
  • 39. 39 Gavriel Salomon, David Perkins and Tamar Globerson (1991). Partners in cognition: extending human intelligence with intelligent technologies. Educational Researcher, 20, 3, pp.il2–9. Learners’ engagement with intelligent technology should not be mindless. On the contrary we should design for… “nonautomatic, effortful and thus metacognitively guided processes”
  • 40. “Embracing Imperfection in Learning Analytics” 40 1. Robust learning design ensures that the activity involving automated feedback is meaningful whether or not the technology always works 2. Explicit encouragement — in student briefings, and in the user interface — to push back if they disagree with the feedback
  • 41. 41 System Integrity > “algorithmic accountability” Learning Informatics Principle 4
  • 42. Remind me, why should I trust this system?
  • 43. Growing public literacy around algorithmic bias and the need for accountability is to be welcomed 43 …but is there anything distinctive about algorithms for teaching and learning that shapes how we frame “accountability”?
  • 44. Stakeholders and key transitions in designing a Learning Analytics system Ethical Principles Educational/Learning Sciences researcher Learning Theory Algorithm Learning Analytics Researcher Educator Learner Learning Outcomes Educational Insights Programmer Software, Hardware User Interface Data
  • 45. Educational/Learning Sciences researcher Programmer Software, Hardware Educator Learner Ethical Principles Algorithm Learning Outcomes Learning Theory Learning Analytics Researcher User Interface Educational Insights Accountability in terms of: Computer Science Data
  • 46. Educational/Learning Sciences researcher Programmer Software, Hardware Educator Learner Ethical Principles Algorithm Learning Outcomes Learning Theory Learning Analytics Researcher User Interface Educational Insights Accountability in terms of: Data Science Data Training Data
  • 47. Educational/Learning Sciences researcher Programmer Software, Hardware Educator Learner Ethical Principles Algorithm Learning Outcomes Learning Theory Learning Analytics Researcher User Interface Educational Insights Accountability in terms of: User-Centred Design Data Design Process
  • 48. Educational/Learning Sciences researcher Programmer Software, Hardware Educator Learner Algorithm Learning Outcomes Learning Theory Learning Analytics Researcher User Interface Educational Insights Accountability in terms of: Deliberative Democracy Data Ethical Principles
  • 49. Accountability in terms of: Deliberative Democracy? “A Deliberative Mini-Public has two core features: • It is deliberative: participants reach their conclusions or recommendations after receiving information and engaging in a careful and open discussion about the issue or issues before them. • It is a mini-public: its members constitute, so far as possible, a representative subset of the wider population.” (Farrell et al, 2019) • Escobar, O. and Elstub, S. (2017), Forms of Mini-Publics: An introduction to deliberative innovations in democratic practice. The newDemocracy Foundation. • Farrell, D., Curato, N., Dryzek, J.S., Geißel, B., Grönlund, K., Marien, S., Niemeyer, S., Pilet, J.-B., Renwick, A., Rose, J., and Setälä, M. (2019), Deliberative Mini-Publics: Core Design Features. Centre for Deliberative Democracy and Global Governance working paper 2019/5. Canberra, Australia: Centre for Deliberative Democracy and Global Governance. Ethical LA Principles
  • 50. Educational/Learning Sciences researcher Programmer Software, Hardware Educator Learner Ethical Principles Algorithm Learning Outcomes Learning Theory Learning Analytics Researcher User Interface Educational Insights Accountability in terms of: Learning Sciences & Educational Technology Data
  • 51. Remind me, why should I trust this system? So we move from asking...
  • 52. Remind me, why should I trust this system? ...to asking...
  • 53. More detailed version of that analysis… 53 LASI tutorial: http://simon.buckinghamshum.net/2019/11/black-box-learning-analytics
  • 54. 54 ICLS 2018 keynote: h9p://simon.buckinghamshum.net/2018/06/icls2018-keynote Elsewhere I have argued that this constitutes a transition in education’s knowledge infrastructure and discuss different theory à analytics mappings
  • 55. 55 learning informatics How does this lens translate into deploying Analytics/AI for learning? Design for educator and learner agency Design socio-technical systems Design for imperfect computational models System Integrity > algorithmic accountability