Web Analytics for Everyday Learning
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This document discusses web analytics and personal analytics for learning. It describes how web analytics can analyze user activities on websites and online systems. Personal analytics can help users improve their behavior by self-tracking. Learning analytics analyze student activities and data from university systems to provide recommendations and applications like vital signs dashboards for doctors. The goal of analytics for everyday learning (AFEL) is to create theory-backed methods and tools that support self-directed learners in making effective use of online resources according to their goals. A scenario is described of a learner who uses an AFEL dashboard to track her progress on different topics and set goals to focus more on areas she is weaker in, like statistics. Challenges discussed include collecting integrated personal data
![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]](https://image.slidesharecdn.com/wwsss2018-keynote-180802132350/85/Web-Analytics-for-Everyday-Learning-29-320.jpg)
Web Analytics for Everyday Learning
- 1. Web Analytics for Everyday Learning Mathieu d’Aquin - @mdaquin Data Science Institute - datascienceinstitute.ie National University of Ireland Galway Insight Centre for Data Analytics AFEL project (@afelproject)
- 2. Web Analytics (the weird great uncle of web science)
- 3. Web Analytics Website or online system Site owner Web users activities analysis Web user Website or online system activities analysis Personal Analytics
- 4. Personal analytics: What for? - To find ways to improve your behaviour (self-tracking, quantified self) - For transparency on security and privacy (privacy mirror, translucent systems) - To learn from it d’Aquin, Thomas (2012) Consumer Activity Data: Usages and Challenges - http://kmi.open.ac.uk/publications/pdf/kmi-12-03.pdf
- 5. Example: Stephen Wolfram Email data. From http://blog.stephenwolfram.com /2012/03/the-personal-analytics -of-my-life/
- 6. Example: UCIAD and MOLUTI projects Personal analytics dashboard of activities of one student on their university’s systems MOLUTI - browser extension showing basic dashboard of browsing activities in the browser d'Aquin, Elahi, Motta. "Semantic technologies to support the user-centric analysis of activity data." SPOT@ISWC 2011.
- 7. Example: Going broader than the browser d'Aquin, Thomas (2013) "Semantic Web Technologies for Social Translucence and Privacy Mirrors on the Web." In PrivOn@ ISWC. 2013.
- 8. Example: Going broader than the browser
- 9. Example: Going broader than the browser d'Aquin, Elahi, Motta. "Personal monitoring of web information exchange: Towards web lifelogging." WebSci 10.
- 10. Example: EPIFABO
- 11. Example: EPIFABO d'Aquin, Thomas (2013) "Modeling and Reasoning Upon Facebook Privacy Settings." ISWC 2013 Demo.
- 13. Learning Analytics Website or online system Site owner Web users activities analysis Web user Website or online system activities analysis for learning Personal Learning Analytics University systems Students Course manager Learner
- 14. Examples of Learning Analytics Vital for doctors OU Analyse
- 16. Example of Learning Analytics: Recommendation
- 17. Learning (from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 18. Learning (still from a system’s point of view) Learner Platform VLE | Website | Library Assessment | Enrollment School/University
- 19. 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. d'Aquin, et al. (2018). AFEL-Analytics for Everyday Learning. In Companion Proceedings of the The Web Conference 2018
- 20. 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.
- 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. 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
- 23. browser Challenge #1: Collecting data
- 24. browser AFEL Data Platform Extension app Tracker Crawler Crawler Traces and metadata AFEL identifier AFEL identifier Challenge #1: Collecting data
- 25. browser AFEL Data Platform Analytics platform VisualisationAFEL identifier Analysis Extension app Tracker Crawler Crawler AFEL Core Data Model (based on schema.org) Learning indicators Traces and metadata AFEL identifier AFEL identifier Challenge #1: Collecting data Integrated personal data
- 26. 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? Challenge #1: What is learning?
- 27. Cognitive model: Learning and knowledge construction through co-evolution The dynamic processes of learning and knowledge construction from Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
- 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. “constructive friction is the driving force behind learning” -- AFEL Deliverable 4.1, [CK08]
- 30. 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.
- 31. Example - coverage Text analysis Clustering Progress analysis Browser history Learning scopes (topics)
- 35. Example - coverage web programming british isles d'Aquin, et al. (2017) "AFEL: Towards Measuring Online Activities Contributions to Self-directed Learning." In ARTEL@ EC-TEL. 2017.
- 36. Results
- 37. Results
- 38. Results See Analytics for Everyday Learning workshop at EC-TEL 2018
- 39. Conclusion Web Science, including web analytics, social media analysis, learning analytics, is mostly about what a lot of people do in a limited context. Personal (learning) analytics is about analysing what one person does over a large variety of contexts, for their own benefits (efficiency, self-improvement, privacy monitoring). There are many technological challenges to achieve this. There are also many non-technological challenges regarding how we use this (transparency, self-governance, control, etc.)