Data, personal learning and learning analytics

This week’s topic for Stephen Downes’ E-Learning 3.0 MOOC is Data.   From the synopsis that Stephen provides for the week we read that…

…. there are two conceptual challenges associated with this topic: first, the shift in our understanding of content from documents to data; and second, the shift in our understanding of data from centralized to decentralized.

The first shift allows us to think of content – and hence, our knowledge – as dynamic, as being updated and adapted in the light of changes and events. The second allows us to think of data – and hence, of our record of that knowledge – as distributed, as being copied and shared and circulated as and when needed around the world.

To try and make sense of this topic I have watched three videos this week.

Personal Learning vs Personalized Learning: What Needs to Happen Oct 24, 2018 Online Learning 2018, Toronto, Ontario, Contact North. This special briefing explores personal learning as the future of learning, explores why it’s important, the tools which enable personal learning and the significant potential of personal learning as a key to life-long learning and the skills agenda. URL: https://www.youtube.com/watch?v=mVnjet3cKfU

This was the video that most resonated with me and related most to my personal interests. What I like about Stephen’s work is that he doesn’t forget to ask the question ‘why’, i.e. the ‘why’ of learning analytics for learners, rather than just the ‘what’ and ‘how’. In this video Stephen tells us that there are two approaches to learning, personalized (formal learning, which accounts for about 20% of our learning) and personal (informal learning, which accounts for the rest). This slide (7) from his presentation ( https://www.downes.ca/cgi-bin/page.cgi?presentation=497 ) provides a clear overview of the differences.

Stephen then considered how we can support an approach which promotes personal learning through discussion of three major themes: choice, ownership and community. In this video Stephen says of learning analytics that it should be for learners so that they can track and understand their own progress. This would mean, in terms of the three major themes, that we can choose what to work on (create our own learning paths), where to store our data and what data to store; that we own all our data and have control over how it is used; and that we are free to work openly and create our own learning communities with whom we can share our data and from whom we draw support. Learning analytics will help us to keep track of our data (which will be distributed over various locations on the web) and self-monitor our personal progress. Personalized learning, whilst still useful and necessary in certain contexts, does not allow for the autonomy necessary for personal learning. The big question raised by Stephen was ‘how can we make this happen?’ i.e. how can personal learning be promoted and recognised in today’s education contexts.

AI in Education Symposium – Introduction: Oct 24, 2018 Artificial Intelligence and 21st Century Education in Ottawa, my brief introduction and posing of a problem. URL: https://www.youtube.com/watch?v=WENb9N2gnpQ

In this 6-minute video, Stephen introduces the AI in Education Symposium in Toronto. He asks can AI solve the problems of society, since society has now become too complex for its problems to be solved by a few elite, privileged groups? He says that as society gets more complex it becomes increasingly difficult to govern. In the future we will need to teach each other and govern ourselves as a society. We will have to move from a society based on identity, nationalism, religion and language to a society based on consensus and collaborative decision making. The question posed was – Does AI offer us lessons into how to do this? I can see how this is related to the themes developed in the ‘Personal Learning vs Personalized Learning: What Needs to Happen’ video.

Conversation with Shelly Blake-Plock Oct 24, 2018 Week 1 of E-Learning 3.0 with Shelly Blake-Plock, Co-Founder, President and CEO – Yet Analytics. URL: https://www.youtube.com/watch?v=dsmdwnUwKkA

This third video was the E-Learning 3.0 MOOC course video for the week. In this conversation Shelly Blake-Plock described his work in Yet Analytics, a company which focusses exclusively on learning analytics and works with the K-12, corporate and military sectors in the US, to help improve learning content and instruction, and improve the management of data. The system they have developed for tracking learning experiences and performance is known as Experience API (xAPI). Shelly claimed that this system goes beyond how a traditional LMS is able to analyse content and activity. xAPI is able to pull data from the physical world (sensors etc.), mobile devices, games, etc. This data is stored in a secure Learning Record Store, which can then provide automated data visualisations to support learners in understanding their progress.

In watching this third video, it seemed to me that there is a mismatch between Stephen’s aspirations for learner autonomy and the learning analytics systems being developed by Yet Analytics. Questions that were asked by Stephen and others on the course, were:

• How would this work with distributed data (remembering that distributed data allows for choice, ownership and community, as well greater security)?
• Who owns the data/records?
• What are the ethical implications of these developments?
• What are the privacy and governance issues?
• How will the data tell us what learners have learned/understood, as opposed to what they have ‘done’, in terms of number of views, clicks on documents etc.

These are important questions for Yet Analytics to answer if they are really going to provide a system that goes well beyond what a traditional LMS can do and recognises a ‘personal’ learning approach to education.

Finally, as a result of watching these videos and thinking about learning analytics this week, I have wondered what might be the implications of measuring and monitoring everything we do. Is there a danger that it could be taken to excess, such that we treat our bodies like machines, become super-competitive, self-centred and self-absorbed?

Update 31-10-2018

Shelly Blake-Plock has pointed out that there are some errors in what I have written about his work, and has responded to the questions listed above. Please see his comment below.

Related blog posts

There have been some interesting posts from other course participants related to all this. See for example:

Geoff Cain – Week 0: Seimens and Downes on AI – http://geoffcain.com/blog/ai/week-0-seimens-and-downes-on-ai/

Roland Legrand – An Experience API for learning everywhere (also in virtual worlds) – https://www.mixedrealities.com/2018/10/25/an-experience-api-for-learning-everywhere-also-in-virtual-worlds/

Matthias Melcher – #EL30 Alien Intelligence AI – https://x28newblog.wordpress.com/2018/10/24/el30-alien-intelligence-ai/

Laura Ritchie – #el30 Notes Week 1 – https://www.lauraritchie.com/2018/10/25/el30-notes-week-1/

#SOCRMx Week 5: Data Analysis

In this second half of the Introduction to Social Research Methods course the focus shifts from data generation and research methods to data analysis.

 

(Click on image for source)

The main task for this week has been to look at the way in which the authors of two research papers (provided by the course) have analysed their data and presented their subsequent findings. One paper took a qualitative approach to data generation and the other a quantitative approach. Useful prompt questions have been provided to support this task.

For me, more interesting than this task are two points raised in the course text, with the suggestion that these are discussed in our blogs, but to my knowledge no-one has done this.

The first is related to the messiness of research which is drawn to our attention through a quote from Hardy and Bryman’s text – Handbook of Data Analysis – which incidentally is not open access.

This is the quote:

active researchers seldom march through the stages of design, data collection, and data analysis as if they were moving through security checkpoints that allowed mobility in only one direction. Instead, researchers typically move back and forth, as if from room to room, taking what they learn in one room and revisiting what was decided in the previous room, keeping the doors open. (Hardy and Bryman 2004, p.2)

This is very much in keeping with my experience, but I would suggest that it is even more messy than Hardy and Bryman suggest. Richard Feynman talked about living and working with doubt and uncertainty…

… and Paul Feyerabend argued ‘Against Method’. In the Stanford Encyclopedia of Philosophy is written of Feyerabend 

‘…. whereas he had previously been arguing in favour of methodology (a “pluralistic” methodology, that is), he had now become dissatisfied with any methodology. He emphasised that older scientific theories, like Aristotle’s theory of motion, had powerful empirical and argumentative support, and stressed, correlatively, that the heroes of the scientific revolution, such as Galileo, were not as scrupulous as they were sometimes represented to be. He portrayed Galileo as making full use of rhetoric, propaganda, and various epistemological tricks in order to support the heliocentric position.’  

More recently Stephen Downes has also argued against method suggesting that traditional approaches to research do not account for the horribly messy, complex, always changing world in which we are now living and conducting research.  See Digital Research Methodologies Redux for his presentation.

A course like this Introduction to Social Research Methods necessarily presents an orderly sequenced set of resources and activities, but research ‘in the wild’ is far from orderly.

The second point made in the Week 5 course text that stood out for me was this one:

Hardy and Bryman (2004) ……  also discuss data reduction as a core element of analysis (my bold): to analyze or to provide an analysis will always involve a notion of reducing the amount of data we have collected so that capsule statements about the data can be provided. (p.4)

This for me is an enormously significant statement. As McGilchrist says (p. 28 The Master and His Emissary)

The kind of attention we bring to bear on the world changes the nature of the world we attend to ……… Attention changes what kind of a thing comes into being for us: in that way it changes the world.

It follows then that how we choose to reduce the amount of data we have collected will determine what research outcomes ‘come into being’, what we learn from the research and will have implications for how the findings are used.

Both these statements in the Week 5 course materials, concerning the messiness of research and the reduction of data, seem to me to perhaps warrant more attention than they have received in the course.

References

Hardy, M. and Bryman, A. (2004). Introduction: common threads among techniques of data analysis. In Hardy, M. & Bryman, A. Handbook of data analysis (pp. 1-13). : SAGE Publications Ltd. doi: 10.4135/9781848608184

McGilchrist, I. (2010). The Master and his Emissary. The Divided Brain and the Making of the Western World. Yale University Press