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Teaching, Assessment and Learning Analytics: Time to Question Assumptions

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Presented by the Assessment Research Centre
and the Melbourne Centre for the Study of Higher Education

Teaching, Assessment and Learning Analytics: Time to Question Assumptions



Simon Buckingham Shum
Professor of Learning Informatics, and Director of the Connected Intelligence Centre (CIC)
University of Technology Sydney


When: 11.30 -12.30 pm, Wed. 13 Sep 2017
Where: Frank Tate Room, Level 9, 100 Leicester St, Carlton


This will be a non-technical talk accessible to a broad range of educational practitioners and researchers, designed to provoke a conversation that provides time to question assumptions. The field of Learning Analytics sits at the convergence of two fields: Learning (including learning technology, educational research and learning/assessment sciences) and Analytics (statistics; visualisation; computer science; data science; AI). Many would add Human-Computer Interaction (e.g. participatory design; user experience; usability evaluation) as a differentiator from related fields such as Educational Data Mining, since the Learning Analytics community attracts many with a concern for the sociotechnical implications of designing and embedding analytics in educational organisations.

Learning Analytics is viewed by many educators with the same suspicion they reserve for AI or “learning management systems”. While in some cases this is justified, I will question other assumptions with some learning analytics examples which can serve as objects for us to think with. I am curious to know what connections/questions arise when these are shared..


Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, where he was appointed in August 2014 to direct the new Connected Intelligence Centre. Previously he was Professor of Learning Informatics and an Associate Director at The UK Open University’s Knowledge Media Institute. He is active in the field of Learning Analytics as a co-founder and former Vice President of the Society for Learning Analytics Research, and Program Co-Chair of LAK18, the International Learning Analytics and Knowledge Conference. Previously he co-founded the Compendium Institute and Learning Emergence networks. Simon brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI Design Argumentation (PhD, York). He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin, wrote Constructing Knowledge Art (2015). He was recently appointed as a Fellow of The RSA. http://Simon.BuckinghamShum.net

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Teaching, Assessment and Learning Analytics: Time to Question Assumptions

  1. 1. https://twitter.com/Wiswijzer2/status/414055472451575808 1
  2. 2. Simon Buckingham Shum Connected Intelligence Centre • University of Technology Sydney @sbuckshum • http://utscic.edu.au • http://Simon.BuckinghamShum.net Teaching, Assessment and Learning Analytics: Time to Question Assumptions University of Melbourne • Assessment Research Centre & Centre for the Study of Higher Education, 13th Sept. 2017
  3. 3. https://twitter.com/Wiswijzer2/status/414055472451575808 3 “Note: check the huge difference between knowing and measuring…”
  4. 4. Scaleable Precise Quantifiable Reprocessable
  5. 5. 5 I was speaking at this UK ESRC symposium http://codeactsineducation.wordpress.com
  6. 6. 6 I asked Siri to find the conference website… “Find code acts in education”
  7. 7. 7 “Find code acts in education” I asked Siri to find the conference website…
  8. 8. 8 https://www.youtube.com/watch?v=Dlr4O1aEJvI&sns=em
  9. 9. Learning Analytics: a form of computational social science 9 Computing/ Data Sciences Education & Learning Sciences Human-Computer Interaction
  10. 10. What do we mean by “Learning Analytics”? “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” Society for Learning Analytics Research 10 Er, isn’t this what educational researchers have always done?
  11. 11. What do we mean by “Learning Analytics”? “the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” UK Joint Information Systems Committee 11 For details and more definitions see http://www.laceproject.eu/faqs/learning-analytics Er, so Learning Analytics = statistical power tools for educational researchers/ learning scientists, or institutional analysts?
  12. 12. What do we mean by “Learning Analytics”? “The potential of learning analytics is arguably far more significant than as an enabler of data-intensive educational research, exciting as this is. The new possibility is that educators and learners — the stakeholders who constitute the learning system studied for so long by researchers — are for the first time able to see their own processes and progress rendered in ways that until now were the preserve of researchers outside the system.” (p.17) 12 Knight S. & Buckingham Shum, S. (2017). Theory and Learning Analytics. Handbook of Learning Analytics (Chapter 1). Society for Learning Analytics Research. https://solaresearch.org/hla-17
  13. 13. Assumptions… Suspicions… Concerns… Examples… as provocations to conversation 13
  14. 14. Learning Analytics are here, but can promote different educational futures: let’s invent good ones 14 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 learning analytics What kinds of learner activity do the analytics value by tracking? (so what is not valued?) Do the analytics value the same things as the assessment regime? (if not, why will educators or learners care?) Do learners see the analytics? What does this say about the pedagogy? (is it desirable that they change their process mid-course in response to their own and others’ feedback?)
  15. 15. Concerns around Learning Analytics Schools and universities are outsourcing their core business — e.g. resource selection, instructional design, feedback and grading — to black box algorithms 15
  16. 16. A student warning system, somewhere near you… Student: “Being told by the LMS every time I login that I’m at grave risk of failing is stressful. I already informed Student Services about my disability and recent bereavement, and I’m working with my tutor to catch up…”
  17. 17. A student warning system, somewhere near you… University: “Don’t worry it’s nothing personal. It’s just the algorithm.” Student: “Being told by the LMS every time I login that I’m at grave risk of failing is stressful. I already informed Student Services about my disability and recent bereavement, and I’m working with my tutor to catch up…”
  18. 18. A social network visualisation, somewhere near you… Student: “Being picked out like this as some sort of loner makes me feel uncomfortable… I chat with peers all the time in the cafes.”
  19. 19. A social network visualisation, somewhere near you… Student: “Being picked out like this as some sort of loner makes me feel uncomfortable… I chat with peers all the time in the cafes.” University: “Don’t worry it’s nothing personal. It’s just the algorithm.”
  20. 20. Algorithms are generating huge interest in the media, policy, social justice, and academia http://governingalgorithms.org http://datasociety.net
  21. 21. Algorithmic accountability in learning? 21 http://simon.buckinghamshum.net/2016/03/algorithmic-accountability-for-learning-analytics
  22. 22. Concerns around Learning Analytics LMS data often has little to do with learning — why all the attention on those dashboards when our most skilled educators are typically using other platforms creatively and to great effect? 22
  23. 23. We can now aggregate activity from diverse platforms (with student consent) 23 ? ?
  24. 24. Connected Learning Analytics Toolkit K. Kitto, A. Bakharia, M. Lupton, D. Mallet, J. Banks, P. Bruza, A. Pardo, S. Buckingham Shum, S. Dawson, D. Gašević, G. Siemens, & G. Lynch, (2016). The connected learning analytics toolkit. Proc. 6th Int. Conf. Learning Analytics & Knowledge (LAK '16). ACM, New York, NY, USA, 548-549. https://doi.org/10.1145/2883851.2883881 Student dashboard Educator dashboard
  25. 25. 25 CLA Toolkit: Groupwork dashboard We could use this to grade summatively, or as formative feedback to provoke reflection Kitto et al (2017). Designing for student-facing learning analytics. Australasian Journal of Educational Technology, 2017, 33(5)
  26. 26. Concerns around Learning Analytics There’s far more to learning than having students tethered to screens 26
  27. 27. A field exercise… 27
  28. 28. Posture analysis of fieldwork students 28Masaya Okada and Masahiro Tada. 2014. Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environment. Proc. 4th Int. Conf. on Learning Analytics and Knowledge. ACM, New York, NY, USA, 1-10. DOI= http://dx.doi.org/10.1145/2567574.2567579
  29. 29. Sensing co-located teamwork Martinez-Maldonado, R., Power, T., Hayes, C., Abdipranoto, A., Vo, T., Axisa, C., and Buckingham Shum, S. (2017) Analytics Meet Patient Manikins: Challenges in an Authentic Small-Group Healthcare Simulation Classroom. International Conference on Learning Analytics and Knowledge, LAK 2017, 90-94.
  30. 30. Tracking nurses’ movement around the patient Martinez-Maldonado, R., Pechenizkiy, M., Power, T., Buckingham Shum, S., Hayes, C. and Axisa, C. (2017) Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom. International Conference on User Modelling, Adaptation and Personalization, UMAP 2017,
  31. 31. Movement heatmaps –> sequence analysis Martinez-Maldonado, R., Pechenizkiy, M., Power, T., Buckingham Shum, S., Hayes, C. and Axisa, C. (2017) Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom. International Conference on User Modelling, Adaptation and Personalization, UMAP 2017,
  32. 32. Concerns around Learning Analytics The vision of students being continually prodded or reassured by AI agents is antithetical to cultivating student qualities like self-regulation, agency, curiosity, resilience… 32
  33. 33. 33 Assessing learning dispositions: Crick Learning for Resilient Agency survey (CLARA) Deakin Crick, R., Huang, S., Ahmed Shafi, A. and Goldspink, C. (2015). Developing Resilient Agency in Learning: The Internal Structure of Learning Power. British Journal of Educational Studies: 62, (2), 121-160. http://dx.doi.org/10.1080/00071005.2015.1006574 https://utscic.edu.au/tools/clara • http://clara.learningemergence.com
  34. 34. 34 Structural Equation Model underpinning CLARA Deakin Crick, R., Huang, S., Ahmed Shafi, A. and Goldspink, C. (2015). Developing Resilient Agency in Learning: The Internal Structure of Learning Power. British Journal of Educational Studies: 62, (2), 121-160. http://dx.doi.org/10.1080/00071005.2015.1006574
  35. 35. Immediate visual analytic generated by CLARA Feedback to Stimulate Self-Directed Change A framework for reflection and coaching 40 item survey Deakin Crick, R., Huang, S., Ahmed Shafi, A. and Goldspink, C. (2015). Developing Resilient Agency in Learning: The Internal Structure of Learning Power. British Journal of Educational Studies: 62, (2), 121-160. http://dx.doi.org/10.1080/00071005.2015.1006574
  36. 36. Scaling CLARA in UTS n=876 n=957 n=548 n=602 § Approx. 3000 student profiles § For the 921 students with both pre- and post-subject profiles, there were significant positive changes on all 8 dimensions. § We can also derive through cluster analysis significantly different cohort profiles: 4 examples § Next step: explore the relationships of these self-reported profiles to student outcomes
  37. 37. Student Effort (x) vs Grade (y) from teacher observational assessments over the semester: basis for a conversation 37 Nagy, R. (2016). Tracking and visualizing student effort: Evolution of a practical analytics tool for staff and student engagement. Journal of Learning Analytics, 3 (2), 165–193. http://dx.doi.org/10.18608/jla.2016.32.8 UTS:CIC seminar: https://utscic.edu.au/events/niccic-redlands-school-8-june-2016 https://vimeo.com/168306314
  38. 38. 38 From clicks to constructs in MOOCs Defining a C21 capability of Crowd-Sourced Learning (Part of a larger map) Milligan, S. and Griffin, P. (2016). Understanding learning and learning design in MOOCs: A measurement-based interpretation. Journal of Learning Analytics, 3(2), 88– 115. http://dx.doi.org/10.18608/jla.2016.32.5
  39. 39. 39 From clicks to constructs in MOOCs Defining a C21 capability of Crowd-Sourced Learning (Part of a larger map) Milligan, S. and Griffin, P. (2016). Understanding learning and learning design in MOOCs: A measurement-based interpretation. Journal of Learning Analytics, 3(2), 88– 115. http://dx.doi.org/10.18608/jla.2016.32.5
  40. 40. 40 From clicks to constructs in MOOCs Defining a C21 capability of Crowd-Sourced Learning (Part of a larger map) Milligan, S. and Griffin, P. (2016). Understanding learning and learning design in MOOCs: A measurement-based interpretation. Journal of Learning Analytics, 3(2), 88– 115. http://dx.doi.org/10.18608/jla.2016.32.5
  41. 41. 41 From clicks to constructs in MOOCs Defining a C21 capability of Crowd-Sourced Learning (Part of a larger map) Milligan, S. and Griffin, P. (2016). Understanding learning and learning design in MOOCs: A measurement-based interpretation. Journal of Learning Analytics, 3(2), 88– 115. http://dx.doi.org/10.18608/jla.2016.32.5
  42. 42. Learning Analytics for 21st Century Competencies. (Eds.) Buckingham Shum S. & Deakin Crick, R. (2016). Journal of Learning Analytics (Special Section), 3(2), pp. 6-212. http://dx.doi.org/10.18608/jla.2016.32.2 More examples https://utscic.edu.au/lasi-asia-keynote2016
  43. 43. Concerns around Learning Analytics Higher education – at its best – teaches students to craft arguments, and reflect deeply on how they’re developing as learners and professionals (Analytics seem to have nothing to say about such qualities.) 43
  44. 44. ANALYTICAL/CRITICAL rhetorical moves in academic texts BACKGROUND KNOWLEDGE Recent studies indicate … … the previously proposed … … is universally accepted ... NOVELTY ... new insights provide direct evidence ... ... we suggest a new ... approach ... ... results define a novel role ... OPEN QUESTION/MISSING INFORMATION … little is known … … role … has been elusive Current data is insufficient … TREND ... emerging as a promising approach Our understanding ... has grown exponentially ... ... growing recognition of the importance ... CONTRASTING IDEAS … unorthodox view resolves … paradoxes … In contrast with previous hypotheses ... ... inconsistent with past findings ... SIGNIFICANCE studies ... have provided important advances Knowledge ... is crucial for ... understanding valuable information ... from studies SURPRISE We have recently observed ... surprisingly We have identified ... unusual The recent discovery ... suggests intriguing roles SIGNALLING AUTHOR INTENT The goal of this study ... Here, we show ... Altogether, our results ... indicate 44
  45. 45. Academic Writing Analytics: feedback on analytical/argumentative or reflective writing Info https://utscic.edu.au/tools/awa
  46. 46. 46 Highlighted sentences are colour- coded according to their broad type Sentences with Function Keys have more precise functions (e.g. Novelty) CIC’s automated feedback tool: analytical writing Info https://utscic.edu.au/tools/awa
  47. 47. AWA: ANALYTICAL ACADEMIC WRITING (UTS CIVIL LAW) 47Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á., & Wang, X. (Forthcoming). Academic Writing Analytics for Civil Law: Participatory Design through Academic and Student Engagement. International Journal of Artificial Intelligence in Education
  48. 48. AWA: ANALYTICAL ACADEMIC WRITING 48 Roll over sentences with Fkeys for a popup reminding you of their meaning Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á., & Wang, X. (Forthcoming). Academic Writing Analytics for Civil Law: Participatory Design through Academic and Student Engagement. International Journal of Artificial Intelligence in Education
  49. 49. New UI to promote re-drafting in response to feedback Shibani, A., Knight, S., Buckingham Shum, S., & Ryan, P. (2017, Forthcoming). Design and Implementation of a Pedagogic Intervention Using Writing Analytics. In W. Chen et al. (Eds.). Proceedings of the 25th International Conference on Computers in Education, New Zealand.
  50. 50. 50 UTS CIVIL LAW STUDENT VIEWS “takes the emotion out of having your work scrutinized” respondent 12; “it was not embarrassing in the way that it can be when a tutor or marker gives feedback” student 7 reflection notes “I definitely found it useful. It also made me realise that I tend to use bold, certain language in making my point towards the end of each paragraph rather than up front at the beginning (when introducing my point).” Respondent 5 “I realise now what descriptive writing is - the software had quite a bit to say about my lack of justification - also true - pressed for time and difficult circumstances have caused this for me in this instance - good to see it sampled.” Respondent 9 Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á., & Wang, X. (Forthcoming). Academic Writing Analytics for Civil Law: Participatory Design through Academic and Student Engagement. International Journal of Artificial Intelligence in Education
  51. 51. 51 UTS CIVIL LAW STUDENT VIEWS “it is possible to make a clearly stated point in an academic way without using one of the markers …saying that if a paper does not use specified 'signposts' suggests that the writing is not clear and ‘academic’ (see ’tips’ on the results page), constricts writing style. I think it is possible to be clear about your position without explicitly saying 'my position is…’.” "respondent 11 “found that the tool was limited in its ability to pick up on summary sentences. It was able to detect phrases such as ‘ultimately, this essay will conclude,’ or ‘in conclusion,’ but the use of adverbs such as ‘thus,’ and ‘evidently,’ in conclusive statements failed to be recognized.”… “Another limitation is that certain sentences, which were recounts or mere descriptions were deemed important, whilst more substantive parts of the essay containing arguments and original voice failed to be detected.” Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á., & Wang, X. (Forthcoming). Academic Writing Analytics for Civil Law: Participatory Design through Academic and Student Engagement. International Journal of Artificial Intelligence in Education
  52. 52. CIC’s automated feedback tool: reflective writing An early paragraph which is simply setting the scene: Info https://utscic.edu.au/tools/awa
  53. 53. CIC’s automated feedback tool: reflective writing A concluding paragraph moving into professional reflection: Info https://utscic.edu.au/tools/awa
  54. 54. https://twitter.com/Wiswijzer2/status/414055472451575808 54 Thank you! Any assumptions questioned/ answered? Discussion…

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