Learning analytics futures: a teaching perspective
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Talk given by Rebecca Ferguson on 22 November 2018 int Universita Ca'Foscario Venezia at the event Nuovi orizzonti della ricerca pedagogica: evidence-based learning e learning analytics
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Learning analytics futures: a teaching perspective
- 1. Rebecca Ferguson, The Open University, UK Learning analytics futures: a teaching perspective
- 2. Institute of Educational Technology, The Open University, UK
- 3. 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.
- 4. Learning analytics help us to identify and make sense of patterns in the data to improve our teaching, our learning and our learning environments
- 5. Why do educators use 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
- 6. training people for employment? Is teaching about
- 7. From learner to earner • Map progress towards employability skills • Map progress towards desired learning objectives • Accredit progress • Support in-service training across providers • Help students to develop a skills portfolio
- 9. Taking our place in society • Map progress as students become responsible citizens, contributors and innovators • Align analytics with the knowledge, skills and values that the government requires in schools
- 11. Building our community • Map progress towards curriculum areas and subject knowledge constructed by the community • Analyse the learning of groups, rather than individuals • Support learners to design, construct and build together
- 13. Healthy mind, healthy body • Enable individuals to reflect on experiences and build coherent learning journeys • Support self- regulated learning – ‘learning to learn’ – and the individual skills required
- 15. Questioning and learning • Are learners encountering diversity of knowledge and understanding? • Are they developing ways of evaluating argument and information? • Are they developing reliable processes for making sense of the world? • Are they reflecting on their assumptions? • Do they care about truth and knowledge ?
- 18. Learning analytics report card
- 22. Preparing for the future careers2030.cst.org/jobs
- 23. 12 years of change 2012: ‘Year of the MOOC’ 2007: Launch of the iphone 2006: First tweets
- 24. What are we trying to achieve?
- 25. EU priority areas for 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.
- 26. • Shape a renewed curriculum designed around scholarly excellence, focused attention to critical enquiry, multidisciplinary expertise, cross-cultural awareness and 21st century skills. • Realize student potential by ensuring the early engagement of employers and institutional stakeholders and by supporting our students in achieving their best personal and professional development. • Enhance Ca’Foscari’s on-line education services to increase the number of on-line students from zero to 1,000, promote our on- site services and campus life to support a vibrant and unique university life experience. Foster a transformative learning experience
- 27. Learning Analytics Community Exchange (LACE) visions of the future
- 28. Full report bit.ly/28X5tq7 Visions of the future
- 29. Provocation 1: Learners are monitored by their learning environmnent
- 30. Provocation 2: Learners’ personal data are tracked
- 31. Provocation 3: Analytics are rarely used
- 33. Provocation 5: Open systems are widely adopted
- 34. Provocation 6: Learning analytics are essential tools
- 35. Provocation 7: Analytics help learners make the right choices
- 36. Provocation 8: Analytics have largely replaced teachers
- 37. New provocations from Neil Selwyn (Monash University)
- 38. New provocations from Neil 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
- 40. Action for analytics Learning analytics for European education policy Strategy Research and development Infrastructure Context Standards Sklls Outreach
- 41. Strategy • Align work on learning analytics with strategic objectives and priorities • Develop a roadmap for learning analytics • Assign responsibility for development of learning analytics • Identify and build on work in related areas • Build on learning analytics work to develop new priorities
- 42. 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 Image: sheilaproject.eu
- 43. Infrastructure • Increase data-handling capability • Create organisational structures to support use of learning analytics • Develop methods of sharing experience and good practice
- 44. Context • Develop practices that are appropriate to your context • Think about your financial model
- 45. Standards • Adapt and employ interoperability standards • Develop and employ ethical standards, including data protection • Align analytics with assessment practices • Develop a robust quality assurance process • Develop evaluation frameworks
- 46. Skills • Identify the skills required in different areas • Train and support educators to use analytics to support achievement • Develop and support educational leaders to implement these changes • Educate learners to use analytics to support their own achievement
- 47. Outreach • Engage stakeholders throughout the learning analytics process • Promote awareness of learning analytics
- 48. Stay connected solaresearch.org • LAK conferences • LASI events • LACE
Editor's Notes
- Hi, I’m Rebecca Ferguson
- I’m from The Open University in the UK. It’s the biggest university in the UK, and it’s a distance teaching university. We do have a campus – this is the building where I work – but our students study all round the country, and all round the world. This means we have always used data to help with our teaching and learning. For example, we use data to check that our students are doing the activities they should be, that they are on track, and that things are going well
- So, from the time that people first started using learning analytics, we’ve been involved in using them, developing them, and helping to set the agenda. I’m an academic – so I always like to start with a definition. This is the one used by the Society for Learning Analytics Research, SoLAR, which is the international body working in this field. You’ll see I’ve highlighted one word in the definition – ‘optimizing’. That’s because some people stop at the stage of collecting and analyzing data and forget why they’re doing it.
- Here’s another way of saying the same thing. I use this picture to remind me of the limitations of these data. Often, we work with the traces that students leave behind in the virtual learning environment. However, like these traces that have been left behind in the sand, there’s always a lot of information missing.
- Educators use analytics in the way they have used data in the past. In the past, you might have had information from an attendance register, and from a mark book, and from exam results. Perhaps some demographic information about your student’s background and the knoeledge and resources they were likely to have. Analytics let you do this on a larger and more detailed scale. They also give you opportunities to share the data and the interpretaions with students in order to discuss what is happening.
- So, if we are optimizing learning, it’s important to have an idea about what we’re trying to achieve. Here are some of the things you might be trying to achieve in your teaching. I’ve illustrated them with some recent tweets, because these aren’t just abstract idea, these are what people are discussing and doing.
- A favourite one with governments is the idea that the point of education is to train people for jobs. In that case, you might want analytics that do these things. However, remember that lots of people aren’t learning for this reason. There are many reasons that people don’t move into the workplace (for example, caring responsibilities or their own health). We also know that people often go on learning after they have retired.
- This is another favourite of governments, and this is often an area where there is funding available. Here are a couple of examples from the UK,with different perspectives. One is about progress towards global goals, whereas the other is much more centred on being a British citizen
- Not all communities are aligned with national governments. Some are bigger – such as the Catholic community. Some are much smaller.
- Here you need analytics that learners can understand and that they can apply themselves in different situations. You may also need transferable records or ways of saving and considering data.
- And this one is very relevant for the world today. Can people deal with fake news and misinformation?
- If you haven’t encountered analytics before, or you’ve only encountered them via a dashboard on a VLE, it may be difficult to imagine how you could align them with what you are trying to achieve with your teaching. It’s important to remember that people developing analytics tools often start with the data. They think about what data they have already or can collect easily, and then they think about how they can present that to you. They don’t necessarily think very hard about what you might do with it. Sometimes, they end up telling you things you know already, or things you could find out more easily some other way, or things you really don’t need to know. What you need, though, are analytics aligned with what you’re trying to achieve. So here’s a learning analytic tool that is very well known – the Signals tool that was developed at Purdue University. It gathers data about student activity and progress and then gives them a traffic light signal: red, amber or green. Green means all appears to be well. Amber means the system thinks the student may be running into problems. Red means the system has detected clear signs the student is running into problems. Importantly, Signals doesn’t just produce a colour, it also provides suggestions about what the student can do, and these suggestions include discussing progress with a tutor. So this is a system that is designed to detect potential problems as early as possible, and to help students and tutors to remedy those problems
- This is another type of analytic. It’s focused on how students learn together. This image represents students in a forum. Each individual is represented by a red dot, or node. When a student contacts another student, they are connected by a line. I’ve used blue to indicate some of the things happening in this group. Up in the top right are a series of dots with no connections. They’re not talking to anyone in the forum In the middle are some ‘information brokers’. They’re talking to lots of people. In fact, depending on what type of activity has been set, they may be talking too much. There are some potentially low performing students, only talking to one other person, and some potentially high-performing students, talking to several people. To understand a diagram like this, you need to know the context. What activity have the students been set, and what would be appropriate behavior?
- This is an analytic that’s being developed at the University of Edinburgh. It puts more control in the hands of the learners, because they can select what elements of their performance are important to them, and can look at how they are performing from that perspective
- Finally, here’s a different approach that comes from informal learning, and uses data in a different way. iSpot is a tool for identifying living things. You post a picture, and all the details you have, and then members of the community help you to identify it. It’s a very good system – people typically receive an identification within an hour. So here’s a picture I posted, and a couple of the identifications. You’kk see that both identifications agree that this is a speckled bush cricket. However, one says that it’s a juvenile, and one says it’s an adult male. This is an informal learning tool, so there’s no teacher around to tell me the right answer. Instead, I use the analytics built into the system. Those little icons tell me about the people who have posted the identification
- The icons give ma lot of information about the people who have posted the identifications. I can find out how often they engage with iSpot. I can also see how often other people agree with them And I can see where they are expert and where they don’t know so much. For example, this person knows a lot about fungi, but not so much about fish. They obviously aren’t interested in birds or animals. So I can take this information, provided by the system, and use it to help me to decide who the experts are, and whose isentification I should trust.
- 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
- 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
- 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 different visions.
- 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 strong opposition from parents. Issues here of ethics, and of WHY the analytics are being developed and applied
- 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
- 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.
- 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
- 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.
- 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
- These provocations are from Neil Selwyn, Monash University
- A seven-point plan: Action for Analytics in Europe