Data for Learning and Learning with Data
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Presentation at Accenture Dublin - 20/02/2018
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Data for Learning and Learning with Data
- 1. Data for Learning and Learning with Data Mathieu d’Aquin - @mdaquin Data Science Institute National University of Ireland Galway Insight Centre for Data Analytics AFEL project (@afelproject)
- 2. Learning (from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 3. Learning (from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University Edu on
- 4. Education/Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 5. Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 6. Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University This needs to evolve to become more open and connected
- 10. Applications - Simple A very simple map of the buildings of the Open University…. Built in 2 hours… Using data from ordnance survey. b1 b1-addr ess Postcode- mk76aa name “Berrill building” Milton Keynes inDistrict Buckingha mshire inCounty Mk76aa location location lat long 52.024 924 -0.709 726
- 13. Applications - Learning Analytics
- 15. The LinkedUp Data Catalogue
- 16. What is in Education Data?
- 17. A simple model of education Person Learner Author Topic Resource Book OER Course Material Multimedia Material Organisation Institution Course affiliated with associated with created Teacher takesregistered with expert in teaches usesstudies about
- 18. But... Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 19. A simple(r) model of online education/learning Person Learner Author Topic Resource Book OER Course Material Multimedia Material Organisation Institution Course affiliated with associated with created Teacher takesregistered with expert in teaches usesstudies about
- 20. Person Learner Author Topic Resource Book OER Course Material Multimedia Material Organisation Institution Course affiliated with associated with created Teacher takesregistered with expert in teaches usesstudies about A simple(r) model of online education/learning
- 21. Person Learner Author Topic Resource Book OER Course Material Multimedia Material Organisation Institution Course affiliated with associated with created Teacher takesregistered with expert in teaches usesstudies about A simple(r) model of online education/learning
- 22. Person Learner Author Topic Resource Book OER Course Material Multimedia Material Organisation Institution Course affiliated with associated with created Teacher takesregistered with expert in teaches usesstudies about A simple(r) model of online education/learning
- 23. A much simpler model of online (possibly self-directed, possibly informal, possibly incidental) learning Person Resource to learn about interested in Topic about uses contributes tointeracts/colla borates with on relates to relates to
- 24. What can be done with data under this model? 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.
- 25. Scenario 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.
- 26. Scenario 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.
- 27. Challenges How do we recognise learning in (the data of) open, generic unconstrained environments? How do we measure learning in (the data of) open, generic unconstrained environments?
- 28. Cognitive model: Learning and knowledge construction through co-evolution The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
- 29. Cognitive model: Learning and knowledge construction through co-evolution The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
- 30. Cognitive model: Learning and knowledge construction through co-evolution 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]
- 31. Identified types of constructive frictions, indicators of learning (in a given learning scope) - Coverage: Most obvious indicator. How much of the concepts covered by the given learning scope (topic) have been covered by captured learning activities. - Complexity: How the learner difficult at the resources used by the learner in exploring this learning scope. - Diversity: How diverse the resources and activities used by the learner have been in the given learning scope.
- 32. Current results - the AFEL personal analytics app
- 33. Realisation Data collection: Activity streams from specific platforms (e.g. Didactalia) or using browser plugin. Data Enrichment: - Fine grained semantic topic extraction for resources - Computing complexity indexes for textual resources - Using learnt models to estimate gender, age and political orientation of author of resources Data processing: - Clustering to compute learning scopes - Compute indicators - Recommendation based on learning scope and indicators
- 34. Conclusion Using semantic technologies, large scale data management and data analytics is driven by new practices in learning, and can help push those practices further. It can in particular support learners in managing their learning, through self-tracking of learning activities or goals Applicable to open or closed environments, fully independent, self-directed learning, or more formal settings. AFEL tools (there are also others) looking for early adopters for validation and participation in their evolution