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Preparing for the future
The document discusses the role of learning analytics in optimizing learning and teaching through data measurement, analysis, and reporting. It outlines the benefits for both learners and educators, including improved self-monitoring, identification of engagement patterns, and better educational outcomes. Additionally, it emphasizes the need for a strategic approach to implement learning analytics effectively across educational contexts in Europe.
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Preparing for the future
- 1. Rebecca Ferguson, TheOpen University, UK Preparing for the future
- 2. Learning analytics 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. Learning analytics helpus to identify and make sense of patterns in the data to improve our teaching, our learning and our learning environments
- 4. Why do learnersuse analytics? • Monitor their own activities and interactions • Monitor the learning process • Compare their activity with that of others • Increase awareness, reflect and self reflect • Improve discussion participation • Improve learning behaviour • Improve performance • Become better learners • Learn!
- 5. Why do educatorsuse analytics? • Monitor the learning process • Explore student data • Identify problems • Discover patterns • Find early indicators for success • Find early indicators for poor marks or drop-out • Assess usefulness of learning materials • Increase awareness, reflect and self reflect • Increase understanding of learning environments • Intervene, advise and assist • Improve teaching, resources and the environment
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- 11. Preparing for thefuture careers2030.cst.org/jobs
- 12. 12 years ofchange 2012: ‘Year of the MOOC’ 2007: Launch of the iphone 2006: First tweets
- 13. What are we tryingto achieve?
- 14. EU priority areasfor education • Open and innovative education and training, fully embracing the digital era. • Strong support for teachers, trainers, school leaders and other educational staff. • Relevant and high-quality knowledge, skills and competences developed throughout lifelong learning. • Focus on learning outcomes for employability, innovation, active citizenship and well-being and inclusive education, equality, equity, non-discrimination and the promotion of civic competences.
- 15. • Safety andwellbeing: all children and young people are protected from harm and vulnerable children are supported to succeed with opportunities as good as those for any other child. • Educational excellence everywhere: every child and young person can access high- quality provision, achieving to the best of his or her ability regardless of location, attainment and background. • Prepared for adult life: all 19-year- olds complete school or college with the skills and character to contribute to the UK’s society and economy and are able to access high-quality work or study options.
- 17. Charles Sturt University Achievingthe University's vision and objectives involves: • providing an accessible and effective learning environment for all students, regardless of location or mode of study • strengthening learning and teaching partnerships with the professions and industry • promoting, recognising and supporting good practice in learning and teaching.
- 18. Learning Analytics Community Exchange(LACE) visions of the future
- 19. Priority areas foreducation and training 1 9 Bringing together higher education, schools & workplace learning Building networks that outlived the project’s funding period Helping to develop learning analytics capability Creating and sharing resources Developing visions of the future and agreeing how to work towards them http://www.laceproject.eu/
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- 21. Provocation 1: Learners are monitoredby their learning environmnent
- 22. Provocation 2: Learners’ personal dataare tracked
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- 25. Provocation 5: Open systems arewidely adopted
- 26. Provocation 6: Learning analyticsare essential tools
- 27. Provocation 7: Analytics help learnersmake the right choices
- 28. Provocation 8: Analytics have largelyreplaced teachers
- 29. New provocations from NeilSelwyn (Monash University)
- 30. New provocations fromNeil Selwyn • Socially sympathetic design • Transparency of algorithms • Student control • Sharing the profits of learning data • Working towards a better public understanding of analytics • Seeing ethics in terms of power Image: Neil Selwyn, LAK18
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- 32. LAEP Learning analytics forEuropean education policy • What is the current state of the art? • What are the prospects for the implementation of learning analytics? • What is the potential for European policy to be used to guide and support the take-up and adaptation of learning analytics to enhance education in Europe?
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- 34. Strategy • Align workon learning analytics with strategic objectives and priority areas for education and training • Develop a roadmap for learning analytics • Assign responsibility for development of learning analytics • Identify and build on work in related areas and other countries • Build on learning analytics work to develop new priorities Example of a framework for learning analytics: Siemens, G., Gašević, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K. & Baker, R.S.J.d. (2011). Open Learning Analytics: An Integrated and Modularized Platform. Download from solaresearch.org
- 35. Research and development •Develop pedagogy that makes good use of analytics • Develop analytics that address strategic objectives and priorities • Develop technology that enables deployment of analytics • Develop frameworks that enable development of analytics Image: sheilaproject.eu
- 36. Infrastructure • Increase data-handlingcapability • Create organisational structures to support use of learning analytics • Use Evidence Hub to identify areas for development • Develop methods of sharing experience and good practice
- 37. Context • Align learning analyticswork with different sectors of education • Develop practices that are appropriate to different contexts • Identify successful financial models
- 38. Standards • Adapt andemploy interoperability standards • Develop and employ ethical standards, including data protection • Align analytics with assessment practices • Develop a robust quality assurance process • Develop evaluation frameworks
- 39. Skills • Identify theskills required in different areas • Train and support educators to use analytics to support achievement • Train and support researchers and developers to work in this field • Develop and support educational leaders to implement these changes • Educate learners to use analytics to support their own achievement
- 40. Outreach • Engage stakeholders throughoutthe learning analytics process • Support collaboration with commercial organisations • Promote awareness of learning analytics
- 41. Stay connected solaresearch.org •LAK conferences • LASI events • LACE
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Editor's Notes
- #20 The Learning Analytics Community Exchange (LACE) project in Europe has been thinking about the future of learning analytics – which futures we want to work towards and which we want to avoid. To investigate this, we have carried out a Policy Delphi, a form of research designed to elicit a range of exert views on a topic. In this case, we developed eight provocations or visions of the future of learning analytics. Using a survey, we shared these with experts and practitioners around the world and asked them to comment on at least two visions in terms of desirability, feasibility, and actions that would need to be taken.
- #21 The full report on this research is available online at this link. Here, I shall run briefly through the eight provocations to give you an idea of how learning analytics might develop during the next decade
- #22 Provocation 1: Learners are monitored by their learning environments Provocation 1 relates to a world in which almost anything a learner uses can be used to collect data about their activities People saw how this vision could be connected with sensor technology and the Internet of Things. They also raised the issue of Big Brother watching over learners and controlling what they do
- #23 Provocation 2: Learners’ personal data are tracked Provocation 2 deals not with external data but with internal data. Information about where students are looking, how they are reacting to stimuli, what their heart rate is. The picture shows the Mindlfex game in which the blue ball is controlled by the user’s brainwaves – which suggests we are moving towards being able to detect thought patterns Respondents linked this to the notion of the ‘quantified self’, and to activity in the field of medicine. They called for a reliable evidence base, which is an idea found in many of the responses to diffferent visions.
- #24 Provocation 3: Analytics are rarely used Provocation 3 is a negative one from the point of view of learning analytics. It suggests that there will be so many problems and controversial stories that learning analytics are no longer used in ten years time. The image refers to the multi-million dollar inBloom project, funded by the Gates Foundation, which had to close due to srong opposition from parents. Issues here of ethics, and of WHY the analytics are being developed and applied
- #25 Provocation 4: Learners control their own data Provocation 4 is concerned with who owns the data. Should it be owned and controlled by individual learners? Divergent opinions here. Some people think learners should control their data and that organisations should make this possible and desirable. Others think that this will make the data unusable and that it just adds needless extra responsibilities to the work of students
- #26 Provocation 5: Open systems are widely adopted Provocation 5 is to do with getting learning analytics, and their related systems, to talk to each other and to understand each other. It also ties in with building a developer community. In order for this open approach to be possible, a lot of work needs to be done at local and national levels.
- #27 Provocation 6: Learning analytics are essential tools Provocation 6 sees the role for learning analytics increasing, so that all learners are supported by a mound of data However, this requires thought about what it means to learn, how learning takes place, and how learning analytics support that process
- #28 Provocation 7: Analytics help learners make the right choices Provocation 7 sees a positive role for analytics, with control remaining in the hands of the learners. Although this sounds a positive future, respondents could see potential problems.
- #29 Provocation 8: Analytics have largely replaced teachers The final provocation sees analytics replacing teachers Any teacher that can be replaced by a computer deserves to be. This is a rewording by David Thornburg of the original Arthur C Clarke quote (“Teachers that can be replaced by a machine should be.”) Again, this prompts consideration of how learning takes place and how it can best be supported
- #31 These provocations are from Neil Selwyn, Monash University
- #34 From this we came up with a seven-point plan for Action on Analytics in Europe