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The AFEL Project

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Presentation at The Web Conference (WWW 2018) international project track.

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The AFEL Project

  1. 1. The AFEL Project Mathieu d’Aquin | @mdaquin | @afelproject Insight Centre for Data Analytics National University of Ireland, Galway Angela Fessl, Dominik Kowald, Elisabeth Lex and Stefan Thalmann Know Center, Graz, Austria The AFEL Consortium
  2. 2. Analytics for Everyday Learning
  3. 3. Analytics for Everyday Learning = Learning Analytics for online, social, self-directed learning
  4. 4. Learning 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.
  5. 5. Learning 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.
  6. 6. Learning 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
  7. 7. Learner Platform Analytics VLE | Website | Library Assessment | Enrollment School/University Prediction Drop out BI Planning Recommendation 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
  8. 8. But: Everyday Learning Learner Platform Analytics VLE | Website | Library Assessment | Enrollment School/University Prediction Drop out BI Planning Recommendation
  9. 9. But: Everyday Learning Learner Platform Analytics VLE | Website | Library Assessment | Enrollment School/University Prediction Drop out BI Planning Recommendation
  10. 10. Learner Platform Analytics VLE | Website | Library Assessment | Enrollment School/University Prediction Drop out BI Planning Recommendation Sentiment Analysis Collective Intelligence Behaviour Analysis Collaboration Community Support But: Everyday Learning
  11. 11. Objective To create theory-backed methods and tools supporting self-directed learners and the people helping them in making more effective use of online resources, platforms and networks according to their own goals.
  12. 12. as a testbed Objective To create theory-backed methods and tools supporting self-directed learners and the people helping them in making more effective use of online resources, platforms and networks according to their own goals. Cognitive Theories of Learning Technology research Technical developm ent
  13. 13. Guided by scenarios 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.
  14. 14. Guided by scenarios 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.
  15. 15. learner browser Challenge #1: Collecting data
  16. 16. 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
  17. 17. 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
  18. 18. Example : Browser Extension Same model used for Facebook application, Twitter, Didactalia analytics..
  19. 19. 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?
  20. 20. The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015. Understanding learning where it can’t be measured
  21. 21. Understanding learning where it can’t be measured The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
  22. 22. 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
  23. 23. 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.
  24. 24. Example - Analysing topic coverage from traces in web browsing Text analysis Clustering Progress analysis Browser history Learning scopes (topics)
  25. 25. Example - Analysing topic coverage from traces in web browsing
  26. 26. Example - Analysing topic coverage from traces in web browsing
  27. 27. Example - Analysing topic coverage from traces in web browsing
  28. 28. web programming british isles Example - Analysing topic coverage from traces in web browsing
  29. 29. Overview of the technology in AFEL AFEL Data Platform Storage Catalog APIs Data Extractor Data Extractor Data Extractor Recommender services Indicator services Data enrich. & feature extraction Visualisation GNOSS Tools Other platforms Tools
  30. 30. Overview AFEL Data Platform InputAPIs OutputAPIs Target platform AFEL Mobile app AFEL Visual Analytics AFEL Rec. Services enriched activity data and indicators enriched activity data and indicators recommendations activity data resource text and metatada resources and activities
  31. 31. Overview AFEL Data Platform InputAPIs OutputAPIs AFEL Mobile app AFEL Visual Analytics AFEL Rec. Services enriched activity data and indicators enriched activity data and indicators recommendations activity data resource text and metatada resources and activities
  32. 32. Overview AFEL Data Platform InputAPIs OutputAPIs AFEL Mobile app AFEL Visual Analytics AFEL Rec. Services enriched activity data and indicators enriched activity data and indicators recommendations activity data resource text and metatada resources and activities
  33. 33. Results: Advanced Visual Analytics Framework for Everyday Learning Analytics
  34. 34. A customisable mobile application for the self-directed learner
  35. 35. A customisable mobile application for the self-directed learner
  36. 36. Conclusion The Objective of the project… To create theory-backed methods and tools supporting self-directed learners and the people helping them in making more effective use of online resources, platforms and networks according to their own goals. … is being accomplished. The basic theories, data collection, data refinement, data visualisation and integration mechanisms are in place. Many other activities, including work on Data Ethics, echo chambers, author profiling not detailed here.
  37. 37. Examples of other useful outcomes Public data release Learning Analytics Glossary
  38. 38. Conclusion And of course, we are open to requests to test our tools on other platforms and for collaborations, and welcome early adopters!
  39. 39. http://afel-project.eu - @afelproject

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