Archive for the ‘e-learning 2.0’ Category

Conversational learning and evidence based education

September 12th, 2017 by Graham Attwell

I have missed out on this autumn’s conference circuit. I just DJg4lLdXUAAiqw8don’t have the money to pay for fees and travel (let alone beer) in attending these events. I am not sure that I actually miss the conferences themselves, but I do miss meeting friends and catching up with what is going on.

And of course, it is increasingly possible to at least dip in to conferences online these days. What with mobile phones and twitter you can almost watch the slides progressing in real time. This morning I noticed one presentation seemed to be getting a lot of my twitter feed. It was Mike Sharples speaking at the ALTALC tagged conference – it took me some time to suss out the ALC stood for the Active Learning Conference taking place at Anglia Ruskin University.

A couple of slides interested me.The slide above is based on the Open University FutureLearn platform. This sums up perfectly how we have used the platform in the EmployID project for running (sadly not open) courses on the Future of Work for employees from the UK Department for Works and Pensions (the UK Public Employment Service. The evaluation showed the courses to be a great success (more on this tomorrow). But I am not so convinced to what degree the FutureLearn platform helped our pedagogic approach – at best I would say it hindered us less than other MOOC platforms we have used.DJg2tuIXcAA5A_X

The second slide also rings true – at least to my experience in using technology for professional development. It is not always easy to link online professional development to practice. But I am ever more sure this is critical to effective learning. Learning spaced over time is an interesting idea in an age of quick bite learning. Of course it depends learning over how much time. Ideally the learning should evolve in line with the practice – but that is not easy to achieve.

The future of learning is social?

May 16th, 2017 by Graham Attwell

In the stream of tweets passing by on the top of my screen I noticed a link to a article called ‘The end of Formal Learning Content’ by Juliette Denny on the web site.

“Since formal learning content takes up so much time, and is often hit-and-miss, what would happen if we got rid of it completely? Is it possible to have a content-free learning program?”, asks Juliette.

She goes on to say the first casualities of such a change would be training managers. But “a new post of ‘training facilitator’ has just opened up. Although it has similarities to the dusty old ‘training manager’ role, the purpose is quite different. Instead of trying to make people learn, it’s the training facilitator’s job to let people learn. Rather than prescribe a rigid structure, it’ll be up to them to create the right environment and focus on keeping the learners engaged.”

Juliette points to small learning bites for what would still be formal learning but user generated content, discussion and interchange on a social learning platform as the answer for the future.

Nothing wrong with any of this. However, I think it underestimates the degree of culture change and the amount of work in organising social learning. In the EmployID project we have been experimenting with social learning and in particular with the role of participants as learning facilitators themselves. The evaluation results are pretty impressive, especially as people who say they never liked ‘traditional elearning’ but love our courses. But promoting the discourse required for social learning takes some considerable effort. We have been using different MOOC platforms (albeit with limited numbers – the largest had around 400 signed up. The MOOC platforms do not really support social learning and we are casting around for an alternative. And if we were to get truly massive numbers participating, I thing we would need some numbers of moderators to properly support learners.

So I am heartened that the elearning industry is recognising the potential of social learning – if only in a blog article. But I think there is more work to be done in understanding how such learning can be facilitated.


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.


Barcamp at Association of Medical Education in Europe conference

August 1st, 2016 by Graham Attwell

At the end of August I am going to the Association of Medical Education in Europe (AMEE) conference in Barcelona. Although I work more in the construction sector, the Learning Layers project is working to develop technology to support informal learning in two sectors, construction and healthcare. I’ll be joining the Layers team from healthcare at the conference, where we are organising a barcamp as well as an exhibition stand. On the stand we will be presenting a number of apps developed and trialled in the healthcare sector in the UK, including a ‘portfolio’ or evidence building app, Bits and Pieces, and Confer, a communication platform for collaborative work in solving problems and developing policies and procedures. And we will be showing the context aware flexibility of the Learning Toolbox app, originally developed for the construction sector, with a special stack of miniapps developed specifically for AMEE.

AMEE is a big conference, with over 3000 delegates attending annually. Although still hosting traditional paper presentations it is increasingly branching out to support a range of different presentation formats. And I think I am right in saying this is the first barcamp staged at AMEE. Having long been keen on more unconferencing events, it is good to see the larger and more formal conferences experimenting with such ideas. One of our problems is to explain to delegates just what a barcamp is. for that reason, I have hacked together the video above. Its a nice example of reuse of open educational resources. The original German language video was made by the University of Graz to report on a barcamp in Austria they had organised. With their permission I have added a new introduction and ending to the video and English language translation and subtitles.

If you would like to know more about our activities at AIMEE drop me a line. And for those interested here is the ‘rules’ we have written for the barcamp:

1st Rule: You do talk about BarCamp.

2nd Rule: You do blog about BarCamp.

3rd Rule: If you want to present, you must write your topic and name in a presentation slot.

4th Rule: Only three word intros.

5th Rule: As many presentations at a time as facilities allow for.

6th Rule: No pre-scheduled presentations, no tourists (either make a contribution or move on, you should move groups in order to participate).

7th Rule: Presentations will go on as long as they have to or until they run into another presentation slot.

8th Rule: If this is your first time at BarCamp, you HAVE to present. (Ok, you don’t really HAVE to, but try to find someone to present with, or at least ask questions and be an interactive participant.


New book: Empowering Change in European Public Employment Services

July 18th, 2016 by Graham Attwell

employid bookReaders familiar with European Research projects will know how they work. The projects negotiate with the European Commission a DOW – Description of Work – which details the work to be undertaken in each year of the project. It is divided into discrete work packages. Every year the work package provides a (usually over lengthy) report on research and development undertaken which is then presented to a team of expert reviewers who can ‘pass’, recommend changes or ‘fail’ the report. Although obviously large scale multi national research projects need structures and plans. But all too often the work package structure separates research and development activities which should not be separated and the DOW become a restrictive ‘bible’, rather than a guide for action. And despite the large amount of work which goes into preparing the work package reports, they are seldom widely read (if indeed read at all), except by the reviewers.

In the EmployID project which is working with identity transformation in European Public Employment Services (PES), we are doing things differently. The work is structured though cross work package teams, who follow an adapted SCRUM structure. The teams are reviewed at face to face meetings and recomposed if necessary. And this year, instead of producing a series of Work package reports, the project partners have jointly contributed to a book – Empowering Change in Public Employment Services: The EmployID Approach which has just been published and can be downloaded for free.

The introduction to the 244 page PDF book explains the background to the work:

European Public Employment Services (PES) and their employees are facing fundamental challenges to the delivery of efficient and effective services and the need to change their strategies to combat high unemployment, demographic change in increasingly uncertain and dynamic labour markets. This does not only require developing new professional skills related to new tasks, but poses for more profound developmental challenges for staff members.

Three of these changes relate to understanding the changing world of work; a ‘turn’ towards coaching; and the increased importance of relations with employers. The staff need to learn new ways of working, with a major challenge being to enhance the power of collaborative (peer) learning in order to support staff in accomplishing their goals.

All these changes are linked to transforming professional identity, which requires learning on a deeper level that is often neglected by continuing professional development strategies. EmployID makes its contribution here; that PES practitioners’ learning related to professional identity transformation needs to be facilitated through social learning approaches and the nurturing of social learning networks, which include the following:

  • Reflection as a way of turning one’s own and others’ experiences into general insights on multiple levels, both from an individual and a collective perspective

  • Peer coaching as a way of helping learners in changing their behavior through a structured process

  • Social learning programmes as a way of engaging learners with new topics, other perspectives, and conversations around it.

Workplace Learning Analytics for Facilitation in European Public Employment Services

April 29th, 2016 by Graham Attwell

This week I have been at the pre-conference workshops for the Learning analytics conference in Edinburgh. This is my presentation at the workshop on Workplace Learning Analytics. And below is the abstract of my paper together with a link to download the full paper, if you should wish. In the next few days,  I will write up a reflection on the workshops, plus some new ideas that emerged from talking with participants.

The paper is based on early research and practices in developing workplace Learning Analytics for the EU funded EmployID project, focused on identity transformation and continuing professional development in Public Employment Services (PES) in Europe. Workplace learning is mostly informal with little agreement of proxies for learning, 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. Rather than focusing on formal learning, LA in PES needs to be based on individual and collective social practices and informal learning and facilitation processes rather than formal education. Furthermore, there are considerable concerns and restraints over the use of data in PES including data privacy and issues including power relations and hierarchies.

Following a consultation process about what innovations PES would like to pilot and what best meets their needs, PES defined priorities for competence advancement around the ‘resourceful learner’, self-reflection and self-efficacy as core competences for their professional identity transformation. The paper describes an approach based on Social Learning Analytics linked to the activities of the EmployID project in developing social learning including advanced coaching, reflection, networking and learning support services. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The final section of the paper reports on work in progress to build a series of tools to embed SLA within communities and practices in PES organisations.

Download the paper (PDF)

Mobile Learning – the Dream goes on

February 29th, 2016 by Graham Attwell

“What killed the mobile learning dream?” asks John Traxler in an article for Jisc’s Digifest. John goes on to say:

Mobile learning was e-learning’s dream come true. It offered the potential for completely personalised learning to be truly any time, anywhere.

ltbInstead, we’ve ended up with mobile access to virtual learning environments that are being used as repositories. So, in practice, students reading their notes on the bus.

He’s right but I am not sure his reasons are sufficient. The main problem John sees is that when early projects were developed into mobile learning, they were based on supplying participants with digital devices. This was expensive and limited the scale and sustainability of such projects. Now new initiatives are emerging based on BYOD (bring your own Device). This is more sustainable but raises its own questions.

Bring your own device, enabling students to use their own equipment, introduces more questions: is there a specific range of technologies they can bring, what’s the nature of the support offered, and have we got a network infrastructure that won’t fall over when 20,000 students turn up with gadgets? What kind of staff development is needed to handle the fact that not only will the students turn up with many different devices but tomorrow they’ll have changed to even more different devices?

All this is true. And as we prepare to roll out the trial of our Learning Layers project funded Learning Toolbox (LTB) application we are only to aware that as well as looking at the technical and pedagogic application of Learning toolbox, we will have to evaluate the infrastructure support. The use of Learning toolbox has been predicated on BYOD and has been developed with Android, iOS and Microsoft versions. The training centre where the pilot will take place with some 70 apprentices, BauABC, covers a large site and is in a rural area. Telecoms network coverage is flaky, broadband not fast and the wireless network installed to support the pilots is a new venture. So many issues for us to look at there. However in terms of staff development I am more confident, with an ongoing programme for the trainers, but perhaps more importantly I think a more open attitude from construction industry trainers to the use of different technologies than say from university lecturers.

The bigger issue though for me is pedagogy. John hints at this when he talks about mobiles being used to access virtual learning environments that are being used as repositories. The real limitation here is not in the technology or infrastructure but a lack of vision of the potential of mobiles for learning in different contexts. Indeed I suspect that the primary school sector is more advanced in their thing here than the universities. Mobile devices have the potential to take learning into the world outside the classroom and to link practical with more theoretical learning. And rather than merely pushing learning (to be read on the bus although I have never quite understood why mobile learning vendors think everyone travels home by bus), the potential is to create a new ecosystem, whereby learners themselves can contribute to the learning of others, by direct interaction and by the sharing of learning and of objects. Dare I say it – Learning Toolbox is a mobile Personal Learning Environment (at least I hope so). We certainly are not looking to replace existing curricula, neither existing learning technologies. Rather we see Learning Toolbox as enhancing learning experiences and allowing users to reflect on learning in practice. In this respect we are aware of the limitations of a limited screen size and also of the lack of attraction of writing long scripts for many vocational learners. This can be an advantage. Mobile devices support all kinds of gesturing (think Tinder) and are naturally used for multimedia including video and photographs.

So what killed the mobile learning dream. Lack of understanding of its true potential, lack of vision and a concentration of funding and pilot activities with the wrong user groups. That is not to say that mobile learning cannot be used in higher education. But it needs a rethinking of curriculum and of the interface between curriculum, pedagogy and the uses of technology. So the dream is not dead. It just needs more working on!

If you would like to know more about Learning Toolbox or are interesting in demonstration or a pilot please contact me graham10 [at] mac [dot] com

The future of learning at work. How technology is influencing working and learning in healthcare: Preparing our students and ourselves for this future

February 16th, 2016 by Graham Attwell

Over the last few weeks I seem to have been bombarded with calls for submissions for conferences. Most I have ignored on the grounds that they are just too expensive. And if I can’t afford them, working as a relatively senior researcher with project funding, what hope do emerging researchers have of persuading their universities or companies to pay. But tto be honest I am bored with most of the conferences. Formal papers, formally presented with perhaps ten or twenty people in a session and very limited time for discussion. We know there are better ways of learning!

One conference I have submitted an abstract to is AMEE. – the International Association for Medical Education. Apart from short communications, research papers and PhD presentations AMEE invites posters, Pecha Kucha, workshops, points of view and organises a fringe to the conference. Sounds good to me and as you might guess I have submitted a point of view. Here goes (in 300 words precisely) ……

The future of work is increasingly uncertain and that goes just as much for healthcare as other occupations. An ageing population is resulting in increasing demand for healthcare workers and advances in technology and science are resulting in new healthcare applications. At the same time technology promises a revolution in self-diagnosis, whilst Artificial Intelligence and robots may render many traditional jobs obsolete.

So what can we say about healthcare skills for the future and what does it mean for healthcare education. Whilst machines may take over more unskilled work, there is likely to be increasing demand for high skilled specialist healthcare workers as well as those caring for the elderly. These staff need to be confident and competent in using existing technologies and adapting to technologies of the future.

They will need to be self-motivated lifelong learners, resilient and capable of coping with changing occupational identities. They will need to collaborate in multidisciplinary teams leading to a high premium on communication skills.

Present processes of education and training based predominantly on face-to-face courses cannot cope with the needs of lifelong learning. Learning needs to be embedded in everyday work processes. Technology is critical here; ubiquitous connectivity and mobile devices allow context-based learning. The same technologies can promote informal and social learning, learning from peers and sharing experience and knowledge in personal learning networks. Already there are many MOOCs dedicated to medical education. Healthcare professionals are using social media to build informal learning networks. But these are the exceptions not the norm. In the future machine learning algorithms can support individuals wishing to deepen their knowledge, VR to share experiences. Yet although there is a rich potential, medical educators have to steer the process. We need to know what works, what doesn’t, to evaluate, to share. That needs to start now!

Workplace Learning Analytics for Facilitation in European Public Employment Services

February 10th, 2016 by Graham Attwell

Along with colleagues from the EmployID project, I’ve submitted  a paper f to the workshop on Learning Analytics for Workplace and Professional Learning (LA for Work) at Learning Analytics and Knowledge Conference (LAK 2016) in April. Below is the text of teh paper (NB If you are interested, the orgnaisers are still accepting submissions for the workshop.


In this paper, we examine issues in introducing Learning Analytics (LA) in the workplace. We describe the aims of the European EmployID research project which aims to use

Image: Educause

technology to facilitate identity transformation and continuing professional development in European Public Employment Services. We describe the pedagogic approach adopted by the project based on social learning in practice, and relate this to the concept of Social Learning Analytics. We outline a series of research questions the project is seeking to explore and explain how these research questions are driving the development of tools for collecting social LA data. At the same time as providing research data, these tools have been developed to provide feedback to participants on their workplace learning.


Learning Analytics (LA) has been defined as “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.” [1]. It can assist in informing decisions in education education system, promote personalized learning and enable adaptive pedagogies and practices [2].

However, whilst there has been considerable research and development in LA in the formal school and higher education sectors, much less attention has been paid to the potential of LA for understanding and improving learning in the workplace. There are a number of possible reasons for this.

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” [3] 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. Once more key areas such as collaboration tend to be omitted and in focusing on VLEs, a failure to include all the different modes of learning. Ferguson [4]) says that in LA implementation in formal education: “LA is aligned with clear aims and there are agreed proxies for learning.” Critically, much workplace learning is informal with little agreement of proxies for learning. While Learning Analytics in educational settings very often follow a particular pedagogical design, workplace learning is much more 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 [5].  Learning interactions at the workplace are to a large extent informal and practice based and not embedded into a specific and measurable pedagogical scenario.

Pardo and Siemens [6] point out that “LA is a moral practice and needs to focus on understanding instead of measuring.” In this understanding “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 workplace learners can potentially benefit from being exposed to their own and other’s learning processes and outcomes as this potentially allows for better awareness and tracing of learning, sharing experiences, and scaling informal learning practices [5]. LA can, for instance, allow trainers and L & D professionals to assess the usefulness of learning materials, increase their understanding of the workplace learning environment in order to improve the learning environment and to intervene to advise and assist learners. Perhaps more importantly,  it 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.

There have been a number of early attempts to utilise LA in the workplace. Maarten de Laat [7] has developed a system based on Social Network Analysis to show patterns of learning and the impact of informal learning in Communities of Practice for Continuing Professional Development for teachers.

There is a growing interest in the use of MOOCs for professional development and workplace learning. Most (if not all) of the major MOOC platforms have some form of Learning Analytics built in providing both feedback to MOOC designers and to learners about their progress. Given that MOOCs are relatively new and are still rapidly evolving, MOOC developers are keen to use LA as a means of improving MOOC programmes.  Research and development approaches into linking Learning Design with Learning Analytics for developing MOOCs undertaken by Conole [8] and Ferguson [9] amongst others have drawn attention to the importance of pedagogy for LA.

Similarly, there are a number of research and development projects around recommender systems and adaptive learning environments. LA is seen as having strong relations to recommender systems [10], adaptive learning environments and intelligent tutoring systems [11]), and the goals of these research areas. Apart from the idea of using LA for automated customisation and adaptation, feeding back LA results to learners and teachers to foster reflection on learning can support self-regulated learning [12]. In the workplace sphere LA could be used to support the reflective practice of both trainers and learners “taking into account aspects like sentiment, affect, or motivation in LA, for example by exploiting novel multimodal approaches may provide a deeper understanding of learning experiences and the possibility to provide educational interventions in emotionally supportive ways.” [13].

One potential barrier to the use of LA in the workplace is limited data. However, although obviously smaller data sets place limitations on statistical processes, MacNeill [14] stresses the importance of fast data, actionable data, relevant data and smart data, rather than big data. LA, she says, should start from research questions that arise from teaching practice, as opposed to the traditional approach of starting analytics based on already collected and available data. Gasevic, Dawson and Siemens [15]  also draw attention to the importance of information seeking being framed within “robust theoretical models of human behavior” [16]. In the context of workplace learning this implies a focus on individual and collective social practices and to informal learning and facilitation processes rather than formal education. The next section of this paper looks at social learning in Public Employment Services and how this can be linked to an approach to workplace LA.


The European EmployID research project aims to support and facilitate the learning process of Public Employment Services (PES) practitioners in their professional identity transformation process. The aims of the project are born out of a recognition that to perform successfully in their job they need to acquire a set of new and transversal skills, develop additional competencies, as well as embed a professional culture of continuous improvement. However, it is unlikely that training programmes will be able to provide sufficient opportunities for all staff in public employment services, particularly in a period of rapid change in the nature and delivery of such services and in a period with intense pressure on public expenditures. Therefore, the EmployID project aims to promote, develop and support the efficient use of technologies to provide advanced coaching, reflection and networking services through social learning. The idea of social learning is that people learn through observing others behaviour, attitudes and outcomes of these behaviours, “Most human behaviour is learned observationally through modelling from observing others, one forms an idea of how new behaviours are performed, and on later occasions this coded information serves as a guide for action” [17]. Facilitation is seen as playing a key role in structuring learning and identity transformation activities and to support networking in personal networks, teams and organisational networks, as well as cross-organisational dialogue.

Social Learning initiatives developed jointly between the EmployID project and PES organisations include the use of MOOCs, access to Labour Market information, the development of a platform to support the emergence of communities of practice and tools to support reflection in practice.

Alongside such a pedagogic approach to social learning based on practice the project is developing a strategy and tools based on Social Learning Analytics. Ferguson and Buckingham Shun [18] say that 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. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. “As groups engage in joint activities, their success is related to a combination of individual knowledge and skills, environment, use of tools, and ability to work together. Understanding learning in these settings requires us to pay attention to group processes of knowledge construction – how sets of people learn together using tools in different settings. The focus must be not only on learners, but also on their tools and contexts.”

Viewing learning analytics from a social perspective highlights types of analytic that can be employed to make sense of learner activity in a social setting. They go on to introduce five categories of analytic whose foci are driven by the implications of the changes in which we are using social technology for learning [18]. These include 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 saying intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation.

The 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.


Clearly there are close links between the development of Learning Analytics and our approach to evaluation within EmployID. In order to design evaluation activities the project has developed a number of overarching research questions around professional development and identity transformations with Public Employment Services. One of these research questions is focused on LA: Which forms of workplace learning analytics can we apply in PES and how do they impact the learner? How can learning analytics contribute to evaluate learning interventions? Other focus on the learning environment and the use of tools for reflection, coaching and creativity as well as the role of the wider environment in facilitating professional identity transformation. A third focus is how practitioners manage better their own learning and gain the necessary skills (e.g. self-directed learning skills, career adaptability skills, transversal skills etc.) to support identity transformation processes as well as facilitating the learning of others linking individual, community and organizational learning.

These research questions also provide a high level framework for the development of Learning Analytics, embedded within the project activities and tools. And indeed much of the data collected for evaluation purposes also can inform Learning Analytics and vice versa. However, whilst the main aim of the evaluation work is measure the impact of the EmployID project and for providing useful formative feedback for development of the project’s tools and overarching blended learning approach, the Learning Analytics focus is understanding and optimizing learning and the environments in which it occurs.


Whilst the more practical work is in an initial phase, linked to the roll out of tools and platforms to support learning, a number of tools are under development and will be tested in 2016. Since this work is placed in the particular working environment of public administration, the initial contextual exploration led to a series of design considerations for the suggested LA approaches presented below. The access to fast, actionable, relevant and smart data is most importantly regulated by strict data protection and privacy aspects, that are crucial and clearly play a critical role in any workplace LA. As mentioned above power relations and hierarchies come into play and the full transparency to be aspired with LA might either be hindered by existing structures or raise expectations that are not covered by existing organisations structures and process. If efficient learning at the workplace becomes transparent and visible through intelligent LA, what does this mean with regard to career development and promotion? Who has access to the data, how are they linked to existing appraisal systems or is it perceived as sufficient to use the analytics for individual reflection only? For the following LA tools a trade-off needs to be negotiated and their practicality in workplace setting can only be assessed when fully implemented. Clear rules about who has access to the insight gained from LA have to be defined. The current approach in EmployID is thus to focus on the individual learner as the main recipient of LA.   

4.1 Self-assessment questionnaire

The project has developed a self-assessment questionnaire as an instrument to collect data from EmployID interventions in different PES organisations to support reflection on personal development. It contains a core set of questions for cross-case evaluation and LA on a project level as well as intervention-specific questions that can be selected to fit the context. The self-assessment approach should provide evidence for the impact of EmployID interventions whilst addressing the EmployID research questions, e.g. the effectiveness of a learning environment in the specific workplace context. Questions are related to professional identity transformation, including individual, emotional, relational and practical development. For the individual learner the questionnaire aims to foster their self-reflection process. It supports them in observing their ‘distance travelled’ in key aspects of their professional identity development. Whilst using EmployID platforms and tools, participants will be invited to fill in the questionnaire upon registration and then at periodic intervals. Questions and ways of presenting the questionnaire questions are adapted to the respective tool or platform, such as social learning programmes, reflective community, or peer coaching.

The individual results and distance travelled over the different time points will be visualised and presented to individual participants in the form of development curves based on summary categories to stimulate self-reflection on learning. These development curves show the individual learners’ changes in their attitudes and behaviour related to learning and adaptation  in the job, the facilitation of colleagues and clients, as well as the personal development related to reflexivity, stress management and emotional awareness.

4.2 Learning Analytics and Reflective Communities

The EmployID project is launching a platform to support the development of a Reflective in the Slovenian PES in February, 2016. The platform is based on the WordPress Content Management System and the project has developed a number of plug ins to support social learning analytics and reflection analytics. The data from these plugins can serve as the basis for a dashboard for learners providing visualisations of different metrics

4.2.1 Network Maps

This plugin visualizes user interactions in social networks including individual contacts, activities, and topics. The data is visualised through a series of maps and is localised through different offices within the PES. The interface shows how interaction with other users has changed during the last 30 days. This way users can visually “see” how often they interact with others and possibly find other users with whom they wish to interact.

The view can be filtered by different job roles and is designed to help users find topics they may be interested in.

4.2.2 Karma Points

The Karma Points plugin allows users to give each other ‘Karma points’ and ‘reputation points’. It is based both on rankings of posts and of authors. Karma points are temporary and expire after a week but are also refreshed each week. This way users can only donate karma points to a few selected posts in each week. The user who receives a karma point gets the point added to her permanent reputation points.

4.2.3 Reflection Analytics

The Reflection Analytics plugin collects platform usage data and shows it in an actionable way to users. The purpose of this is to show people information in order to let them reflect about their behaviour in the platform and then possibly to give them enough information to show them how they could learn more effectively. The plugin will use a number of different charts, each wrapped in a widget in order to retain customizability.

One chart being considered would visualise the role of the user’s interaction in the current month in terms of how many posts she wrote, how many topics she commented on and how many topics she read compared to the average of the group.  This way, users can easily identify whether they are writing a similar number of topics as their colleagues. It shows change over time and provides suggestions for new activities. However, we also recognise that comparisons with group averages can be demotivating for some people.

4.3 Content Coding and Analysis

The analysis of comments and content shared within the EmployID tools can provide data addressing a number of the research questions outlined above.

A first trial of content coding used to analyse inputs into a pilot MOOC held in early 2015 using the FutureLearn platform resulted in rich insights about aspects of identity transformation and learning from and with others. The codes for this analysis were created inductively based on [19] and then analysed according to success factors for identity transformation. Given that identity transformation in PES organisations is a new field of research we expect new categories to evolve over time.

In addition to the inductive coding the EmployID project will apply deductive analysis to investigate the reflection in content of the Reflective Community Platform following a fixed coding scheme for reflection [20].

Similar to the coding approach applied for reflective actions we are currently working on a new coding scheme for learning facilitation in EmployID. Based on existing models of facilitation (e.g. [21]) and facilitation requirements identified within the PES organisations, a fixed scheme for coding will be developed and applied the first time for the analysis of content shared in the Reflective Community platform.

An important future aspect of content coding is going one step further and exploring innovative methodological approaches, trialing with a machine learning approach based on (semi-) automatic detection of reflection and facilitation in text. This semi-automatic content analysis is a prerequisite for reflecting analysis back to learners as part of learning analytics, as it allows the analysis of large amounts of shared content, in different languages and not only ex-post, but continually in real time.

4.4 Dynamic Social Network Analysis

Conceptual work being currently undertaken aims to bring together Social Network Analysis and Content Analysis in an evolving environment in order to analyze the changing nature and discontinuities in a knowledge development and usage over time. Such a perspective would not only enable a greater understanding of knowledge development and maturing within communities of practice and other collaborative learning teams, but would allow further development and improvements to the (online) working and learning environment.

The methodology is based on various Machine Learning approaches including content analysis, classification and clustering [22], and statistical modelling of graphs and networks with a main focus on sequential and temporal non-stationary environments [23].

To illustrate changes of nature and discontinuities at the level of social network connectivity and content of communications in a knowledge maturing process “based on the assumption that learning is an inherently social and collaborative activity in which individual learning processes are interdependent and dynamically interlinked with each other: the output of one learning process is input to the next. If we have a look at this phenomenon from a distance, we can observe a knowledge flow across different interlinked individual learning processes. Knowledge becomes less contextualized, more explicitly linked, easier to communicate, in short: it matures.” [24]


In this paper we have examined current approaches to Learning Analytics and have considered some of the issues in developing approaches to LA for workplace learning, notably that learning interactions at the workplace are to a large extent informal and practice based and not embedded into a specific and measurable pedagogical scenario. Despite that, we foresee considerable benefits through developing Workplace Learning Analytics in terms of better awareness and tracing of learning, sharing experiences, and scaling informal learning practices.

We have outlined a pedagogic approach to learning in European Public Employment Services based on social learning and have outlined a parallel approach to LA based on Social Learning Analytics. We have described a number of different tools for workplace Learning Analytics aiming at providing data to assist answering a series of research questions developed through the EmployID project. At the same time as providing research data, these tools have been developed to provide feedback to participants on their workplace learning.

The tools are at various stages of development. In the next phase of development, during 2016, we will implement and evaluate the use of these tools, whilst continuing to develop our conceptual approach to Workplace Learning Analytics.

One essential part of this conceptual approach is that supporting learning of individuals with learning analytics is not just as designers of learning solutions how to present dashboards, visualizations and other forms of data representation. The biggest challenge of workplace learning analytics (but also learning analytics in general) is to support learners in making sense of the data analysis:

  1. What does an indicator or a visualization tell about how to improve learning?
  2. What are the limitations of such indicators?
  3. How can we move more towards evidence-based interventions

And this is not just a individual task; it requires collaborative reflection and learning processes. The knowledge of how to use learning analytics results for improving learning also needs to evolve through a knowledge maturing process. This corresponds to Argyris & Schön’s double loop learning [25]. Otherwise, if learning analytics is perceived as a top-down approach pushed towards the learner, it will suffer from the same problems as performance management. These pre-defined indicators (through their selection, computation, and visualization) implement a certain preconception which is not evaluated on a continuous basis by those involved in the process. Misinterpretations and a misled confidence in numbers can disempower learners and lead to an overall rejection of analytics-driven approaches.


EmployID – “Scalable & cost-effective facilitation of professional identity transformation in public employment services” – is a research project supported by the European Commission under the 7th Framework Program (project no. 619619).


[1] SoLAR(2011).Open Learning Analytics: An Integrated & Modularized Platform. WhitePaper.Society for Learning Analytics Research. Retrieved from

[2] Johnson, L. Adams Becker, S., Estrada, V., Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium

[3] Duval E. (2012) Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012,

[4] Ferguson, R. (2012) Learning analytics: drivers, developments and challenges. In: International Journal of Technology Enhanced Learning, 4(5/6), 2012, pp. 304-317.

[5] Ley T. Lindstaedt S., Klamma R. and Wild S. (2015) Learning Analytics for Workplace and Professional Learning,

[6] Pardo A. and Siemens G. (2014) Ethical and privacy principles for learning analytics in British Journal of Educational Technology Volume 45, Issue 3, pages 438–450, May 2014

[7] de Laat M. & Schreurs (2013) Visualizing Informal Professional Development Networks: Building a Case for Learning Analytics in the Workplace, In American Bahavioral Scientist

[8] Conole G. (2014) The implciations of open practice, presentation, Slideshare,

[9] Ferguson (2015) Learning Design and Learning Analytics, Presentation, Slideshare

[10] Adomavicius, G. and Tuzhilin, A. (2005) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734-749.

[11] Brusilovsky, P. and Peylo, C. (2003) Adaptive and intelligent Web-based educational systems. In P. Brusilovsky and C. Peylo (eds.), International Journal of Artificial Intelligence in Education 13 (2-4), Special Issue on Adaptive and Intelligent Web-based Educational Systems, 159-172.

[12] Zimmerman B. J, (2002) Becoming a self-regulated learner: An overview, in Theory into Practice, Volume: 41 Issue: 2 Pages: 64-70

[13] Bahreini K, Nadolski & Westera W. (2014) Towards multimodal emotion recognition in e-learning environments, Interactive Learning environments, Routledge,

[14] MacNeill, S. (2015) The sound of learning analytics, presentation, Slideshare,

[15] Gašević, D., Dawson, S., Siemens, G. (2015) Let’s not forget: Learning Analytics are about learning. TechTrends

[16] Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55 (3), pp 249-70

[17] Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall.

[18] Buckingham Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Educational Technology & Society, 15 (3), 3–26

[19] Mayring, P. (2000). Qualitative Content Analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 1(2). Retrieved from

[20] Prilla M, Nolte A, Blunk O, et al (2015) Analyzing Collaborative Reflection Support: A Content Analysis Approach. In: Proceedings of the European Conference on Computer Supported Cooperative Work (ECSCW 2015).   

[21] Hyland, N., Grant, J. M., Craig, A. C., Hudon, M., & Nethery, C. (2012). Exploring Facilitation Stages and Facilitator Actions in an Online/Blended Community of Practice of Elementary Teachers: Reflections on Practice (ROP) Anne Rodrigue Elementary Teachers Federation of Ontario. Copyright© 2012 Shirley Van Nuland and Jim Greenlaw, 71.   

[22] Yeung, K. Y. and Ruzzo W.L. (2000). An empirical study on principal component analysis for clustering gene expression data. Technical report, Department of Computer Science and Engineering, University of Washington.

[23] Mc Culloh, I. and Carley, K. M. (2008). Social Network Change Detection. Institute for Software Research. School of Computer Science. Carnegie Mellon University. Pittsburgh, PA 15213. CMU-CS-08116.

[24] R. Maier, A. Schmidt. Characterizing Knowledge Maturing: A Conceptual Process Model for Integrating E-Learning and Knowledge Management In: Gronau, Norbert (eds.): 4th Conference Professional Knowledge Management – Experiences and Visions (WM ’07), Potsdam, GITO, 2007, pp. 325-334.

[25] Argyris, C./ Schön, D. (1978): Organizational Learning: A theory of action perspective. Reading.

What is the political and social habit(u)s of present day universities?

January 18th, 2016 by Graham Attwell

I like Cristina Costa’s blog, “Is technology changing learning habit(u)s?” (and not only because she cited me). Cristina says how her study on students’ digital practices shows how students’ learning habitus (their histories/experiences with education) have not changed that much in the formal setting, even when they are presented with new pedagogical approaches. It is not so much an issue of their digital competence but an issue that the informal uses of technology do not simply transfer into formal contexts.

Students, she says, “have a feeling for the ‘academic game’ and do their best to adjust to the field’s rules in order to succeed in it.” It seems to me their was always something of a game in academia and especially in undergraduate education. Even in the early 1970s we had well developed strategies for getting through exams (for instance I undertook a rather more in depth study of past exam questions than I did of the overall curriculum and it worked well for me).

But there are more profound contradictions in today’s higher education system. On the one hand universities are supposed to be about education and learning – as expressed through Humboldt’s idea of Allgemeine Bildung—or well-rounded education—to ensure that each person might seek to realize the human potentialities that he possessed as a unique individual or more modern appeals for a broad liberal education (unless such an education can be seen as improving their employability). On the other hand in the UK students are paying substantial fees for a system designed to provide them with a qualification to realise the so called graduate wage premium in the world of work. In such a situation it is little wonder that students are reluctant to participate in the innovative pedagogies – described by Cristina as  Freirean and Deweyan type of pedagogical approaches – designed for them to explore ideas and knowledge – quite simply they want the knowledge and skills they need to pass the exams and thus justify the expenditure. In this situation students will readily adopt productivity apps – office tools, citation databases, revision apps etc – and of course will use technology for social purposes and entertainment. But I am afraid asking them to use social software for learning within the political and social habit(u)s of present day universities may be going to far.

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    News Bites

    Learning about technology

    According to the University Technical Colleges web site, new research released of 11 to 17-year-olds, commissioned by the Baker Dearing Educational Trust, the charity which promotes and supports University Technical Colleges (UTCs), reveals that over a third (36%) have no opportunity to learn about the latest technology in the classroom and over two thirds (67%) admit that they have not had the opportunity even to discuss a new tech or app idea with a teacher.

    When asked about the tech skills they would like to learn the top five were:

    Building apps (45%)
    Creating Games (43%)
    Virtual reality (38%)
    Coding computer languages (34%)
    Artificial intelligence (28%)

    MOOC providers in 2016

    According to Class Central a quarter of the new MOOC users  in 2016 came from regional MOOC providers such as  XuetangX (China) and Miríada X (Latin America).

    They list the top five MOOC providers by registered users:

    1. Coursera – 23 million
    2. edX – 10 million
    3. XuetangX – 6 million
    4. FutureLearn – 5.3 million
    5. Udacity – 4 million

    XuetangX burst onto this list making it the only non-English MOOC platform in top five.

    In 2016, 2,600+ new courses (vs. 1800 last year) were announced, taking the total number of courses to 6,850 from over 700 universities.

    Jobs in cyber security

    In a new fact sheet the Tech Partnership reveals that UK cyber workforce has grown by 160% in the five years to 2016. 58,000 people now work in cyber security, up from 22,000 in 2011, and they command an average salary of over £57,000 a year – 15% higher than tech specialists as a whole, and up 7% on last year. Just under half of the cyber workforce is employed in the digital industries, while banking accounts for one in five, and the public sector for 12%.

    Number students outside EU falls in UK

    Times Higher Education reports the number of first-year students from outside the European Union enrolling at UK universities fell by 1 per cent from 2014-15 to 2015-16, according to data released by the Higher Education Statistics Agency.

    Data from the past five years show which countries are sending fewer students to study in the UK.

    Despite a large increase in the number of students enrolling from China, a cohort that has grown by 12,500 since 2011-12, enrolments by students from India fell by 13,150 over the same period.

    Other notable changes include an increase in students from Hong Kong, Singapore and Malaysia and a fall in students from Saudi Arabia and Nigeria.

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