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AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning

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presentation at the ARTEL workshop at EC-TEL 2017

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AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning

  1. 1. AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning Alessandro Adamou, Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju, Ilire Hasani-Mavriqi, Peter Holtz, Joachim Kimmerle, Dominik Kowald, Elisabeth Lex, Susana López Sola, Ricardo A. Maturana, Vedran Sabol, Pinelopi Troullinou, Eduardo Veas Mathieu d’Aquin | @mdaquin | @afelproject Insight Centre for Data Analytics National University of Ireland, Galway
  2. 2. Learnign Analytics According to Wikipedia (and past LAK CFPs, and some papers from relevant people) Learning analytics is 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.
  3. 3. Learnign Analytics According to Wikipedia (and past LAK CFPs, and some papers from relevant people) Learning analytics is 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.
  4. 4. Learnign Analytics According to Wikipedia (and past LAK CFPs, and some papers from relevant people) Learning analytics is 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. not only students not only the classroom, university, library or VLE
  5. 5. However, typically, Learning Analytics is... A university uses data on the students and their activities collected through the institution's information systems with the goal to predict their success so they can improve help them improve…. … and improve their teaching, offering, environments as well
  6. 6. But: Everyday Learning
  7. 7. But: Everyday Learning
  8. 8. Mais : Everyday Learning
  9. 9. Challenge #1: Collecting data eLearning platform (e.g. moddle) learner analyst teacher activities generating traces traces and metadata resources and metadata analyse learner identifier activities generating traces, with (sometimes) different identifiers ? analyse
  10. 10. learner browser Challenge #1: Collecting data
  11. 11. Difficulté #1: D'où viennent les données learner activités générant des traces et (des fois, différents) identifiants browser AFEL Data Platform Extension app Tracker Crawler Crawler Traces and metadata AFEL identifier and local identifier AFEL identifier and local identifier
  12. 12. learner activités générant des traces et (des fois, différents) identifiants browser AFEL Data Platform Analytics platform VisualisationAFEL identifier Analysis Integrated personal data Extension app Tracker Crawler Crawler AFEL Core Data Model (based on schema.org) Learning indicators Traces and metadata AFEL identifier and local identifier AFEL identifier and local identifier Challenge #1: Collecting data
  13. 13. Example : Browser Extension Same model used for Facebook application, Twitter, Didactalia analytics..
  14. 14. Challenge #2: Indicators of learning? Maximising what? Minimising what? teacher analyst Ratio : students’ success cost in effort/resources (?) learner In the context of informal, self-directed learning, what is success? What are the relevant notions of effort and cost?
  15. 15. The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015. Understanding learning where it can’t be measured
  16. 16. Understanding learning where it can’t be measured The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
  17. 17. The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015. “constructive friction is the driving force behind learning” -- AFEL Deliverable 4.1, [CK08] Understanding learning where it can’t be measured
  18. 18. Indicators of learning, based on measuring friction! So to favour activities that generate a constructing fiction.. What kinds of frictions? - In topic: In what way the activity introduces topics/themes/concepts that have not been seen before? - In complexity: In what way the activity introduces a further level of complexity which was not accessible before and requires further efforts. - In view: In what way the activity introduces new points of view on an already encountered topic, covering a different aspect.
  19. 19. Example - Analysing topic coverage from traces in web browsing Text analysis Clustering Progress analysis Browser history Learning scopes (topics)
  20. 20. Example - Analysing topic coverage from traces in web browsing
  21. 21. Example - Analysing topic coverage from traces in web browsing
  22. 22. Example - Analysing topic coverage from traces in web browsing
  23. 23. web programming british isles Example - Analysing topic coverage from traces in web browsing
  24. 24. Integration
  25. 25. Next steps The AFEL platform is in place and working, collecting data from the Didactalia platform and from early (alpha) users of the extractor tools (the browser extensions, facebook app, etc), but the technical challenges are still there. Indicators need to be better understood: Which ones are useful? How to calculate them? How to present them? Needs feedback from end-users.
  26. 26. AFEL Resources Public data release Learning Analytics Glossary
  27. 27. http://mdaquin.net - @mdaquin http://afel-project.eu - @afelproject http://afel-project.eu/questionnaire
  28. 28. Jane is 37 and works as an administrative assistant in a local medium-sized company. As a hobbies, she enjoyed sewing and cycling in the local forests. She is also interested in business management, and is considering either developing in her current job to a more senior level or making a career change. Jane spends a lot of time online at home and at her job. She has friends on facebook with whom she shares and discusses local places to go biking, and others with whom she discusses sewing techniques and possible projects, often through sharing youtube videos. Jane also follows MOOCs and forums related to business management, on different topics. She often uses online resources such as Wikipedia and online magazine on the topics. At school, she was not very interested in maths, which is needed if she want to progress in her job. She is therefore registered on Didactalia, connecting to resources and communities on maths, especially statistics. Jane has also decided to take her learning seriously: She has registered to use the AFEL dashboard through the Didactalia interface. She has also installed the browser extension to include her browsing history, as well as the facebook app. She has not included in her dashboard her emails, as they are mostly related to her current job, or twitter, since she rarely uses it. Jane looks at the dashboard more or less once a day, as she is prompted by a notification from the AFEL smartphone application or from the facebook app, to see how she has been doing the previous day in her online social learning. It might for example say “It looks like you progressed well with sewing yesterday! See how you are doing on other topics…” Jane, as she looks at the dashboard, realises that she has been focusing a lot on her hobbies and procrastinated on the topics she enjoys less, especially statistics. Looking specifically at statistics, she realises that she almost only works on it in Friday evenings, because she feels guilty of not having done much during the week. She also sees that she is not putting as much effort into her learning of statistics as other learners, and not making as much progress. She therefore makes a conscious decision to put more focus on it. She adds the dashboard goals of the form “to work on statistics during my lunch break every week day” or “to have achieved a 10% progress compared to now by the same time next week”. The dashboard will remind her how she is doing against those goals as she go about her usual online social learning activities. She also gets recommendation of things to do on Didactalia and Facebook based on the indicators shown on the dashboard and her stated goals.

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