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Learning Analytics: understand learning and support the learner

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Presentation at the NUI Galway library - Research Data Management Group. 02/05/2018

Publicado en: Datos y análisis
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Learning Analytics: understand learning and support the learner

  1. 1. Learning Analytics: understand learning and support the learner Mathieu d’Aquin - @mdaquin Data Science Institute National University of Ireland Galway Insight Centre for Data Analytics AFEL project (@afelproject)
  2. 2. 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.
  3. 3. 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. data analytics applied to data from learning activities
  4. 4. Learning (from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
  5. 5. Learning analytics: Basics Example: Vital for Doctor Dashboard comparing engagement of junior doctors with learning resources, and the effect on their “performance”.
  6. 6. Learning analytics: Prediction Example: OU Analyse Predicting student success based on engagement with learning resources and demographic information.
  7. 7. Learning analytics: Exploration Example: Student location Clustering students based on their location and the subjects to which they enroll.
  8. 8. Learning analytics: Interpretation Example: Understanding how sequences of modules are chosen Using sequence mining and formal concept analysis. d'Aquin, Mathieu, and N. Jay. "Interpreting data mining results with linked data for learning analytics: motivation, case study and directions." In Proceedings of the Third International Conference on Learning Analytics and Knowledge, LAK 2013.
  9. 9. But... 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.
  10. 10. But... 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.
  11. 11. But... 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. i.e. not only students in the classroom/on campus!
  12. 12. Learning (from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University Edu on
  13. 13. Education/Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
  14. 14. Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
  15. 15. Parenthesis Learner Platform VLE | Website | Library Assessment | Enrollment School/University This needs to evolve to become more open and connected
  16. 16. data.open.ac.uk
  17. 17. Applications - Recommendation
  18. 18. Applications - Recommendation
  19. 19. Back to… Learner Platform VLE | Website | Library Assessment | Enrollment School/University
  20. 20. 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.
  21. 21. 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.
  22. 22. 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.
  23. 23. 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?
  24. 24. Cognitive model: Learning and knowledge construction through co-evolution The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
  25. 25. Cognitive model: Learning and knowledge construction through co-evolution The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
  26. 26. Cognitive model: Learning and knowledge construction through co-evolution “constructive friction is the driving force behind learning” -- AFEL Deliverable 4.1, [CK08]
  27. 27. 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.
  28. 28. Current results - the AFEL personal analytics app
  29. 29. What about ethics? This is about behavioural analysis and supporting behavioural changes for learners… ethics questions obviously relevant. Not only about privacy: self-regulation, black-box effect, data-bias, unbalanced access need to be considered. Basic question: Is it OK to process student data for analysing retention, success, learning design, learning behaviours? Reverse question: We already have all those data. Is it OK not to use it to provide the best possible chances of success to our student. Bobbie Eicher et al., Jill Watson Doesn’t Care if You’re Pregnant: Grounding AI Ethics in Empirical Studies, AIES 2018
  30. 30. What about ethics? ‘Ethics in Design’ for Data Science Dialectic The process is based on a conversational approach between data and critical social scientists throughout the project’s life-cycle. Reflective Ethical concerns are not pre-fixed; they may emanate from any stage of the project; thus, constant reflexivity on activities and researchers is needed. Creative, not disruptive The objective of this process is to achieve a positive impact on the research, increase its value addressing ethics throughout the project’s life-cycle. All- encompassing Ethical concerns appear as much in the research activities as in their outcomes, their use and exploitation; the process needs to expand on all stages. d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  31. 31. Conclusion Collecting Data about learning Data Analytics Results Interpretation Intervention
  32. 32. Conclusion Collecting Data about learning Data Analytics Results Interpretation Intervention
  33. 33. Conclusion Collecting Data about learning Data Analytics Results Interpretation Intervention
  34. 34. Conclusion Collecting Data about learning Data Analytics Results Interpretation Intervention Educational psychology Education Science Ethics
  35. 35. Thank you! @mdaquin mathieu.daquin@nuigalway.ie mdaquin.net @afelproject afel-project.eu

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