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Towards Tangible Trusted Learning Analytics

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Keynote was given at TU Delft teaching lab

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Towards Tangible Trusted Learning Analytics

  1. 1. Prof. Dr. Hendrik Drachsler @hdrachsler Towards Tangible Trusted Learning Analytics Learning Analytics Workshop, 08.04.2019, TU Delft, The Netherlands
  2. 2. WhoAmI Hendrik Drachsler Professor of Educational Technologies Research topics Recommender Systems Learning Analytics Multimodal Data for learning Computational Psychometrics Application domains Schools HEI Medical education
  3. 3. Educational Technologies Team Nicole Elker Function: Management Assistent Daniel Biedermann Function: PhD student Sambit Praharaj Function: PhD student George Ciordas- Hertel Function: PhD student Sebastian Wollny Function: PhD student Dr. Jan Schneider Function: PostDoc Atezaz Ahmad Function: PhD student Ioana Jivet Function: PhD student Daniele Dimitri Function: PhD student Marcel Schmitz Function: PhD student Dr. Maren Scheffel Function: PostDoc Hector Pijeira Diaz Function: PhD student Superhero’s
  4. 4. 1. Definition of trust and LA 2. Fears of Learning Analytics 4. Approaches towards Trusted Learning Analytics 3. Human- Centered Design Lecture structure
  5. 5. What are learning analytics for you?
  6. 6. Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society. http://ifets.info/journals/15_3/4.pdf Learning Analytics
  7. 7. Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society. http://ifets.info/journals/15_3/4.pdf Learning Analytics
  8. 8. Sophistican model Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector – Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf 1 2 3 4 5 Learning Analytics
  9. 9. 9
  10. 10. How do you define trust?
  11. 11. Oxford Dictionary Trust is about a firm belief in the reliability, truth, or ability of someone or something. A trustful relation is mutually based on: • openness • truth • reliability • integrity • belief • faith • freedom of suspicion Picture by Terry Johnston https://www.flickr.com/photos/powerbooktrance/466709245/ Multiple definitions of Trust Trust = a multidimensional and multidisciplinary construct Various Contexts • oneself and others • organizations • intelligent systems • automation • money or political power
  12. 12. Niklas Luhmann. Trust and power. John Willey & Sons (1979). A definition of Trust Luhmann defined ‘TRUST’ as a way to cope with risk, complexity, and a lack of system understanding. For Luhmann the concept of trust compensates for insufficient capabilities for full understanding the complexity of the world. Picture by: https://twitter.com/ niklasluhmann
  13. 13. https://en.wikipedia.org/wiki/Ni klas_Luhmann Trust in Learning Analytics • Data subjects face uncertainty e.g. when receiving outcomes of learning analytics. • Data subjects can not fully understand the complexity of learning analytics. • Data subjects take a risk and making oneself vulnerable by feeding learning analytics with personal data. Following Luhmann, we define trust as a social phenomenon with the following characteristics: To gain trust from data subjects we need to demonstrate Transparency, Reliability, and Integrity. As a return the data subjects might ‘choose’ to trust us.
  14. 14. 1. Definition of trust and LA 2. Fears of Learning Analytics 4. Approaches towards Trusted Learning Analytics 3. Human- Centered Design Lecture structure
  15. 15. People are afraid of AI (in TEL) Learning Analytics: Dystopia
  16. 16. Examples why people don’t trust
  17. 17. Keynote Neil Selwyn @ LAK 2018, Sydney, Australia Learning Analytics has a trust problem … Learning Analytics
  18. 18. … because Learning Analytics has the potential of becoming a high stakes assessment. Keynote Neil Selwyn @ LAK 2018, Sydney, Australia Learning Analytics
  19. 19. Being in the next industrial revolution means we are in an education system, where the norms, relationships and ways of teaching and learning are impacted. • Authority: Public -> Private Influence and power are redistributed • New (AI) actors: Feedback to students from machines • Data ownership: Increased access for some may mean reduced access for others • False-truths: Early products with simplistic reasoning don’t represent what learning is really about https://commons.wikimedia.org/w iki/File:Coalbrookdale_loco.jpg Education in the Industrial Revolution
  20. 20. Open algorithms Transparent indicators No automated decisions Full access to data Knowing who accesses your data Feedback culture Unknown algorithms Unknown data collection Automated decisions No access to raw data No control who uses it Assessment culture Black box vs. White box
  21. 21. • Right to be informed • Right of access • Right to rectification • Right to erasure • Right to restrict processing • Right to data portability • Right to object automated decision making Do your Learning Technology systems support these rights? Hands-up! GDPR 2018
  22. 22. 1. Definition of trust and LA 2. Fears of Learning Analytics 4. Approaches towards Trusted Learning Analytics 3. Human- Centered Design Lecture structure
  23. 23. Some things are already on its way http://www.open.ac.uk/students/charter/ess ential-documents/ethical-use-student-data- learning-analytics-policy# https://www.jisc.ac.uk/sites/default/file s/jd0040_code_of_practice_for_learni ng_analytics_190515_v1.pdf
  24. 24. Drachsler, H. & Greller, W. (2016). Privacy and Analytics – it’s a DELICATE issue. A Checklist to establish trusted Learning Analytics. 6th Learning Analytics and Knowledge Conference 2016, April 25-29, 2016, Edinburgh, UK. Online at: http://www.laceproject.eu/ethics-privacy/ Some things are already on its way
  25. 25. http://www.sheilaproject.eu Yi-Shan Tsai, Pedro Manuel Moreno-Marcos, Kairit Tammets, Kaire Kollom, and Dragan Gašević. 2018. SHEILA policy framework: informing institutional strategies and policy processes of learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). ACM, New York, NY, USA, 320-329. DOI: https://doi.org/10.1145/31703 58.3170367 Some things are already on its way There is no other Educational Technology discipline like Learning Analytics that critically works on social implications of their outcomes and addresses institutional development.
  26. 26. 1. Definition of trust and LA 2. Fears of Learning Analytics 4. Approaches towards Trusted Learning Analytics 3. Human- Centered Design Lecture structure
  27. 27. • Design-Based Research (DBR) • AB– testing Barab, S. A. (2014). Design-based research: a methodological toolkit for engineering change. In K. Sawyer (ed.) Handbook of the Learning Sciences, Vol 2, (pp. 233-270), Cambridge, MA: Cambridge University Press. Participatory Design-Process
  28. 28. Moodle environment
  29. 29. • TLA is the first GDPR 2018 conform Big Data infrastructure followed a value-based design approach • Joined project with GU, DIPF und OU • Among ‘traditional‘ learning data we also aim to collect multimodal data. Trusted Learning Analytics Infrastructure
  30. 30. Trusted Learning Analytics Dashboard
  31. 31. Declaration of Consent
  32. 32. Self-Reflection Phase SEREne Dashboard Performance PhaseForethought Phase
  33. 33. How to design your Trusted Learning Analytics TACTIC Cube Trusted Analytics Cube to Teach Institutional Change
  34. 34. 35 Stakeholders 1. Interviews with students (n=46) 2. Survey on Learning Analytics (n=166) 3. Group Concept Mapping Study (n=101, 46) 4. Feedback from students, and teachers on dashboards
  35. 35. Stakeholders
  36. 36. 37 Reflection Prediction Objectives
  37. 37. Objectives
  38. 38. 39 Scheffel, M., Ternier, S., & Drachsler, H. (2016a). The Dutch xAPI Specification for Learning Activities (DSLA) – Registry. Retrieved from http://bit.ly/DutchXAPIreg http://www.laceproject.eu/blog/xapi-dsla/ Educational Data
  39. 39. 40 Educational Data
  40. 40. 41 Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist. Technologies
  41. 41. 42 Technologies
  42. 42. Drachsler, H. & Greller, W. (2016). Privacy and Analytics – it’s a DELICATE issue. A Checklist to establish trusted Learning Analytics. LAK 2016, April 25-29, Edinburgh, UK. Engelfriet, A., Jeunink, E., Manderveld, J. (2015). Learning analytics onder de Wet bescherming persoonsgegevens Constraints
  43. 43. Constraints
  44. 44. 45 Interpretation skills Drachsler, H., Stoyanov, S., d'Aquin, M., Herder, E., Dietze, S., & Guy, M. (2014, 16-19 September). An Evaluation Framework for Data Competitions in TEL. 9th European Conference on Technology-Enhanced Learning (EC-TEL 2014), Graz, Austria.
  45. 45. 46 Interpretation skills
  46. 46. 47 Critical thinking 1.Data literacy 2.Agency 3.Privacy understanding
  47. 47. 48 Critical thinking
  48. 48. Trusted Learning Analytics Cube
  49. 49. Take home messages 1. We need to actively develop and apply learning analytics tools to have informed discussion what are the effects on the stakeholders. 2. We need participatory design approaches to involve all stakeholders in learning analytics and train their agency and data literacy skills. 3. We have an opportunity through the GDPR and the stakeholder discourse in point 2 to design more humanistic Trusted Learning Analytics.
  50. 50. Learning Analytics & Knowledge Conference 2020 http://lak20.solaresearch.org
  51. 51. 52 Questions? @hdrachsler drachsler@dipf.de https://www.linkedin.com/in/hendrikdrachsler

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