Archive for the ‘learning Analytics’ Category

Data and the future of universities

August 2nd, 2018 by Graham Attwell

I’ve been doing quite a lot of thinking about how we use data in education. In the last few years two things have combined – the computing ability to collect and analyse large datasets, allied to the movement by many governments and administrative bodies towards open data.

Yet despite all the excitement and hype about the potential of using such data in education, it isn’t as easy as it sounds. I have written before about issues with Learning Analytics – in particular that is tends to be used for student management rather than for improving learning.

With others I have been working on how to use data in careers advice, guidance and counselling. I don’t envy young people today in trying to choose and  university or college course and career. Things got pretty tricky with the great recession of 2009. I think just before the banks collapsed we had been putting out data showing how banking was one of the fastest growing jobs in the UK. Add to the unstable economies and labour markets, the increasing impact of new technologies such as AI and robotics on future employment and it is very difficult for anyone to predict the jobs of the future. And the main impact may well be nots o much in new emerging occupations,or occupations disappearing but in the changing skills and knowledge required n different jobs.

One reaction to this from many governments including the UK has been to push the idea of employability. To make their point, they have tried to measure the outcomes of university education. But once more, just as student attainment is used as a proxy for learning in many learning analytics applications, pay is being used as a proxy for employability. Thus the Longitudinal Education Outcomes (LEO) survey, an experimental survey in the UK, users administrative data to measure the pay of graduates after 3, 5 and 0 years, per broad subject grouping per university. The trouble is that the survey does not record the places where graduates are working. And once thing we know for a certainty is that pay in most occupations in the UK is very different in different regions. The LEO survey present a wealth of data. But it is pretty hard to make any sense of it. A few things stand out. First is that UK labour markets look pretty chaotic. Secondly there are consistent gender disparities for graduates of the same subject group form individual universities. The third point is that prior attainment before entering university seems a pretty good predictor of future pay, post graduation. And we already know that prior attainment is closely related to social class.

A lot of this data is excellent for research purposes and it is great that it is being made available. But the collection and release of different data sets may also be ideologically determined in what we want potential students to be able to find out. In the same way by collecting particular data, this is designed to give a strong steer to the directions universities take in planning for the future. It may well be that a broader curriculum and more emphasis on process and learning would most benefits students. Yet the steer towards employability could be seen to encourage a narrower focus on the particular skills and knowledge employers say they want in the short term and inhibit the wider debates we should be having around learning and social inclusion.

 

Proxies, learning, deschooling society and annotation

May 11th, 2018 by Graham Attwell

Ivan_Illich_drawingSipping a glass of wine on the terrace last night, I thought about writing an article about proxies. I’ve become a bit obsessed about proxies, ever since looking at the way Learning Analytics seems to so often equate learning with achievement in examinations.

But then by chance this morning I ended up looking at the text of Ivan Illich’s 1969 publication ‘Deschooling Society‘. And I found in the first chapter Illich talks about about how we “confuse teaching with learning, grade advancement with education, a diploma with competence, and fluency with the ability to say something new.

He goes on to say pupils’ “imagination is “schooled” to accept service in place of value. Medical treatment is mistaken for health care, social work for the improvement of community life, police protection for safety, military poise for national security, the rat race for productive work. Health, learning, dignity, independence, and creative endeavour are defined as little more than the performance of the institutions which claim to serve these ends, and their improvement is made to depend on allocating more resources to the management of hospitals, schools, and other agencies in question.”

This seems an apposite comment on how the use and analysis of big data is being developed in the present period.

I stumbled on the Illich quote from a Twitter link to an exercise on the CLMOOC lets be creative together website. They ask “What would Ivan Illich think about CLMOOC?” and go on to suggest “we find activities like this all the more enjoyable and enriching when a variety of voices join the conversation. So this is an open invitation to the internet to join us as we use Hypothes.is to annotate an online copy of Deschooling Society together.”

I have not seen Hypothes.is before but it looks pretty nifty. I have never understood just why collective annotation has never quite taken off. It seems to me a great format for sharing and developing knowledge together. And I think Illich would have liked it.

Designing Learner Dashboards

May 2nd, 2018 by Graham Attwell


The UK Jisc are really good at producing on line reports of workshops and meetings (something which I am not!). This is one of the presentations from the Student Experience Experts Group meeting, two of which  events held every year to share the work of the student experience team at Jisc and to offer opportunities for feedback and consultation on current activities. The Jisc web page provides a brief summary of the meeting and all of the presentations. I picked this one by Liz Bennett from the University of Huddersfield because the issue of how to design dashboards is one which perplexes me at the moment.

Student satisfaction unrelated to learning behaviour and academic performance

March 13th, 2018 by Graham Attwell

I seem to spend a lot of time lately moaning about bad data practices. About approaches to learning analytics which appear to be based on looking at what data is available and the trying to think out what the question is. And particularly over the different proxies we use for learning.

So, I particularly liked the report in THE of the inaugural lecture by Professor Rienties at the UK Open Universitity’s Institute of Educational Technology. Professor Rienties outlined the results of a study that examined data on 111,256 students on 151 different modules at his institution. He found that student satisfaction, one of the most common used proxies for learning and achievement, is “unrelated” to learning behaviour and academic performance. According to THE:

Significantly higher student satisfaction was found in modules in which students received large amounts of learning materials and worked through them individually, than in courses where students had to collaborate and work together.

However, the best predictor for whether students actually passed the module was whether there were collaborative learning activities, such as discussion forums and online tuition sessions.

Students who were “spoon-fed” learning materials also spent less time in the virtual learning environment, were less engaged, and were less likely to remain active over time than their peers engaged in more collaborative activities.

Black Box Learning Analytics? Beyond Algorithmic Transparency

June 14th, 2017 by Graham Attwell

I guess we are all a bored with PowerPoint presentations these days. But when done well, presnetations can be brilliant for questioning what we are doing and should be doing. What are Algorithms? asks Simon Buckingham Shum, Professor of Learning Informatics / Director, Connected Intelligence Centre UTS. According to Paul Dourish in The Social Lives of Algorithm they are abstract rules for transforming data which to exert influence require programming as executable code operating on data structures running on a platform in an increasingly distributed architecture. Simon goes on to question the intentional secrecy technical illiteracy complexity of infrastructure that make algorithms opaque and looks at their growing impact in education.

 

More thoughts on Workplace Learning Analytics

April 18th, 2017 by Graham Attwell

termination-110301_1920I have been looking at the potential of Learning Analytics (LA) for professional development of employees in European Public Employment services as part of the European funded EmployID project. Despite interest, particularly from Learning and Development personnel within the employment services, Learning Analytics, has made only limited impact and indeed reflects the slow take up of LA in the workplace as a whole.

The reasons for this are myriad. Universities and schools have tended to harvest existing data drawn from Virtual Learning Environments (VLEs) and to analyse that data to both predict individual performance and undertake interventions which can for instance reduce drop-out rates. The use of VLEs in the workplace is limited and “collecting traces that learners leave behind” (Duval, 2012) may fail to take cognizance of the multiple modes of formal and informal learning in the workplace and the importance of key indicators such as collaboration. Ferguson (2012) says that in LA implementation in formal education: “LA is aligned with clear aims and there are agreed proxies for learning.” The most commonly agreed proxy of learning achievement is achievement of outcomes in terms of examinations and assignments. Yet in the workplace, assignment driven learning plays only a limited role, mostly in formal courses and initial vocational education and training.

Workplace learning is driven by demands of work tasks or intrinsic interests of the learner, by self-directed exploration and social exchange that is tightly connected to processes and the places of work (Ley at al, 2015). Learning interactions at the workplace are to a large extent informal and practice based and not embedded into a specific and measurable pedagogical scenario.

In present Learning Analytics developments, there appears to be a tension between measuring and understanding. Pardo and Siemens (2014) say “learners are central agents and collaborators, learner identity and performance are dynamic variables, learning success and performance is complex and multidimensional, data collection and processing needs to be done with total transparency.” This poses particular issues within the workplace with complex social and work structures, hierarchies and power relations.

Despite these difficulties we remain convinced of the potential value of Learning Analytics in the workplace and in Public Employment Service organisations. If used creatively, Learning Analytics can assist learners in monitoring and understanding their own activities and interactions and participation in individual and collaborative learning processes and help them in reflecting on their learning. Furthermore, LA offers a potential approach to gaining rapid feedback to trainers and learning designers and data can be a tool for researchers in gaining a better understanding of learning processes and learning environments.

There is some limited emerging research into Workplace Learning Analytics and Social Learning analytics which offer at least pointers towards developing on such potential. Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings, taking into account both formal and informal learning environments, including networks and communities. Buckingham Shum, S., & Ferguson, R., (2012) suggest social network analysis focusing on interpersonal relations in social platforms, discourse analytics predicated on the use of language as a tool for knowledge negotiation and construction, content analytics particularly looking at user-generated content and disposition analytics can be developed to make sense of learning in a social setting.

Such an approach to Social Learning Analytics links to the core aims of the EmployID project to support and facilitate the learning process of PES practitioners in their professional identity development by the efficient use of technologies to provide social learning including advanced coaching, reflection, networking and learning support services. The project focuses on technological developments that make facilitation services for professional identity transformation cost-effective and sustainable by empowering individuals and organisations to engage in transformative practices, using a variety of learning and facilitation processes.

It should also be noted that although Learning Analytics has been linked to the collection and analysis of ‘big data’, MacNeill (2016) stresses the importance of fast data, actionable data, relevant data and smart data. LA, she says, should start from research questions that arise from teaching practice, as opposed to the more common approach of starting analytics based on already collected and available data.

Learning Analytics has been the subject on ongoing discussion in the EmployID project and particularly with the PES organisations. Although a number of PES organisations are interested in the possibility of adopting LA, it is not a major priority for them at present and they are aware of the constraints outlined above. Our initial experiences with sentiment analysis confirm this general interest as well as its limitations with public organisations. It has also became apparent that there are major overlaps between the Social Analytics approach and the tools and approaches we have been developing for evaluation. Our work in evaluation encompasses looking at interpersonal relations in social platforms, discourse analytics based on the EmployID MOOCs as well as learners own mapping of their progress through the self-assessment questionnaire.

We recognise that this data can be valuable for PES employees in supporting reflection on learning. But rather than seeking to develop a separate dashboard for reporting on data, we are attempting to embed representations of learning within the context in which the learning takes place. Thus, the social platform allows users to visualise their different interactions through the platform. Other work, such the facilitation coding scheme, does not yet allow real time analytics. But if proven successful as a research approach to understanding and supporting learning, then it could potentially be automated or semi-automated to provide such real time feedback.

Machines against humans?

March 17th, 2017 by Graham Attwell

I expect to see more of this debate in the future. Richard Palmer (Tribal) and Sheila MacNeil (Glasgow Caledonian University) had a debate at the Jisc Digifest17 about whether learning analytics interventions should always be mediated by a human being. Richard (for machines) and Sheila (for humans), speak about their thoughts on the topic in the Digifest studio with Robert and Louisa.

Learning Analytics and the Peak of Inflated Expectations

January 15th, 2017 by Graham Attwell

hypecycleHas Learning Analytics dropped of the peak of inflated expectations in Gartner’s hype cycle?  According to Educause ‘Understanding the power of data’ is still there as a major trend in higher education and Ed Tech reports a KPMG survey which found that 41 percent of universities were using data for forecasting and predictive analytics.

But whilst many universities are exploring how data can be used to improve retention and prevent drop outs, there seems little pretence any more that Learning Analytics has much to do with learning. The power of data has somehow got muddled up with Management Analytics, Performance Analytics and all kinds of other analytics – but the learning seems to have been lost. Data mining is great but it needs a perspective on just what we are trying to find out.

I don’t think Learning analytics will go into the trough of despair. But i think that there are very real problems in working out how best we can use data – and particularly how we can use  data to support learning. Learning analytics need to be more solidly grounded in what is already known about teaching and learning. Stakeholders, including teachers, learners and the wider community, need to be involved in the development and implementation of learning analytics tools. Overall, more evidence is needed to show which approaches work in practice and which do not.

Finally, we already know a great deal about formal learning in institutions, or at least by now we should do. Of course we need to work at making it better. But we know far less about informal learning and learning which takes place in everyday living and working environments. And that is where I ultimately see Learning analytics making a big difference. Learning Analytics could potentially help us all to self directed learners and to achieve the learning goals that we set ourselves. But that is a long way off. Perhaps if Learning analytics is falling off the peak of expectations that will provide the space for longer term more clearly focused research and development.

 

Learning Analytics for Workplace and Professional Learning

September 19th, 2016 by Graham Attwell

There is a small but growing community emerging in Europe around the potential and use of Learning analytics in the workplace. And there are still places available for a free workshop entitled ‘Learning Analytics for Workplace and Professional Learning’ on Friday 23 September in Leeds in the UK.

The workshop is being organised around two main activities.During the morning all the participants will shortly present their research work or the research questions they are working on. Thus, we can find common problems, synergies and potential collaborations in future projects. During the afternoon we can work on smaller groups to discuss some community building mechanisms: follow-up workshops (maybe at LAK’17), potential grant applications, creation of an on-line community, collaboration in current or future projects, collaboration with other researchers or research communities.

More information can be found in this PDF document. And to register, please send an email to: Tamsin Treasure-Jones (t [dot] treasure-jones [at] leeds [dot] ac [dot] uk)

Workplace Learning Analytics workshop

September 7th, 2016 by Graham Attwell

It is not easy developing a community around Workplace Learning Analytics but there are some signs of emerging interest.

On 23 September 2016, in Leeds, there is an open workshop on Workplace Learning Analytics, sponsored by the Learning Layers project. The invitation to the workshop runs as follows.

Up to now, the Learning Analytics community has not paid much attention to workplace and professional scenarios, where, in contrast to formal education, learning is driven by demands of work tasks and intrinsic interest of the learner. Hence, learning processes at the workplace are typically informal and not related to a pedagogical scenario. But workplace and professional learners can benefit from their own and others’ learning outcomes as they will potentially increase learning awareness, knowledge sharing and scaling-up learning practices.

Some approaches to Learning Analytics at the workplace have been suggested. On the one hand, the topic of analytics in smart industries has extended its focus to some learning scenarios, such as how professionals adopt innovations or changes at the workplace; on the other hand, the Learning Analytics community is increasing its attention to informal scenarios, professional training courses and teaching analytics. For this reason, we consider that it a great moment to collect all this interest to build up a community related to workplace and professional Learning Analytics.

  • We invite you to take part in the workshop which will have two main activities:
    During the morning all the participants will shortly present their research work or the research questions they are working on. Thus, we can find common problems, synergies and potential collaborations in future projects.
  • During the afternoon we can work on smaller groups to discuss some community-building mechanisms: follow-up workshops (maybe at LAK’17), potential grant applications, creation of an on-line community, collaboration in current or future projects, collaboration with other researchers or research communities… etc.

To register, please send an email to: Tamsin Treasure-Jones (t [dot] treasure-jones [at] leeds [dot] ac [dot] uk)

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