Archive for the ‘Wales Wide Web’ Category

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.
Abstract

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)

The future of work and changing occupational identities

April 24th, 2016 by Graham Attwell

The debate over the future of work, long running in research circles but kicked into public consciousness amongst others a Oxford University study titled ‘The Future of Employment: How susceptible are jobs to computerisation’ suggesting over 40 per cent of jobs are at threat in the next 11 years due to technology, emgineercontinues. In truth there is little agreement from economists and labour market specialists. Some claim techn0logy is leading to more jobs, some that it is destroying jobs and still other that it is neutral. Some claim technology is leading to jobs being deskilled, others the reverse.

I like a recent blog post entitled ‘More on digitalisation and skills: What happens within occupations?’, by Guillermo Montt on the OECD Skills and Work web site. The article says that “as technology enters the workplace, the tasks related to a job and an occupation change” citing  Alexandra Spitz-Oener (2006) who found that in Germany, occupations in the 2000s require more complex skills than in 1979 and that this change is more pronounced in occupations that adopted computers. Although something of a simplification, that finding is largely born out in analysis of the USA O*NET data. The article also draws attention to research by James Bessen published in his recent book ‘Learning by Doing: The Real Connection between Innovation, Wages and Wealth‘. “He follows the evolution of occupations over time and claims that accelerated technological change has implications for inequality within occupations with more and more occupations becoming winner-take-all markets.” Essentially, as new technology is introduced pay and opportunities in occupations bifurcate with a few taking high high, pay levels and more taking home lower pay. “In occupations requiring above-median computer use, the 90th to 50th percentile wage ratio has risen by 0.2% per year but has remained stagnant in occupations with below-median computer use. Workers who stay ahead of the curve, those who learn by doing, reap the wage benefits of technological change.”

This has major implication for training and continuing professional development. CPD has traditionally been organised through courses. But as we have already found in in the EmployID project working with employees in European Public Employment Services, traditional course delivery is both too slow to respond to change and even more problematic is unable to deliver the volume of training required. The approach adopted in EmployID is both to look at using new technologies for learning and for promoting informal learning in the workplace but also to center on changing occupational identities. For instance there is a very different occupational identity associated with a print graphic designer than todays web designer. But the ability to change occupational identities may be shaped by previous learning experiences and by motivation as well as the ability to reflect on both individual and group learning. Within EmployID we are exploring how Learning Analytics can bets be deployed to assets people in reflection (Reflection Analytics) and to assist in transforming identities to deal with such change. I am presenting this work next week at a LAKs pre conference workshop in Glasgow and will publish by slides on this blog.

More thoughts on labour markets

April 12th, 2016 by Graham Attwell

Predicting the future of labour markets is not easy at the best of times. And this is not the best of times. The problems include the long lasting effects of the financial crash, the impact of government austerity policies (and non impact of qualitative easing) as well as rapid changes in the way we work and in the technologies we are using.

Essentially future labour markets are modelled using existing labour markets, with the proviso of different scenarios according to disruption. At the moment disruptions are seen to be overriding the base model, resulting in much uncertainty.This is a big issue for young people setting out on a career or indeed for those thinking of changing jobs or of entering education  and training.

The real problem with modelling is that there is no consensus on what is happening with today’s labour markets. Lately  this debate has spilled out from more academic and economic journals into the popular press, with predictions of a severe squeeze on middle skilled work, especially office work, due to the introduction of robots, machine learning and artificial intelligence. Yet a new  study by Dr Andrea Salvatori of the Institute for Social and Economic Research calls such concerns into doubt.

Although she recognises a bifurcation of labour markets with a decline of middle skilled jobs, rather than robots, the cause, she suggests, is the expansion in university education, “which has led to a tripling in the share of graduates among employees, accounting for the entire growth in top-skilled occupations, as well as a third of the decline in middling occupations.”

“In parallel, the relative performance of wages in high-skill occupations has deteriorated relative to mid-skill ones, indicating that the supply of workers for these jobs outpaced demand and contributed to the continuing shift from the middle to the top. These facts are highly suggestive that the improvement in the education of the workforce has contributed significantly to the reallocation of employment from mid- to high-skill occupations.”

Andrea Salvatori says that far from being threatened by technology the wages of middle skilled occupations have risen in line with high skilled professions, which she suggests may be due to the increased use of technology.

This debate is important. It suggests that rather than the disruption by technology (which it is always presumed as inevitable) it is government policies over education and training that are responsible for the shrinkage in middle skilled jobs. It could also be suggested that that lack of such jobs may in part be to blame fo the persistently low rate of increase in productivity in the UK, especially when compared with Germany which has continued to train for middle skilled jobs through its apprenticeship system.

 

The future of work – myths and policies

March 29th, 2016 by Graham Attwell

I like this blog post by Robert Peal entitled ‘A Myth for Teachers: Jobs That Don’t Exist Yet’. The article looks at the origins of the idea that the top 10 in-demand jobs in 2010 didn’t exist in 2004 and its later variant that 60 per cent of the jobs for children in school today have not been invented. In both cases he found it impossible to track these statement in any reliable research. Of course these are myths. But often such myths can be tracked back to quite prosaic political objectives.

For a long time, the European Union has pushed the idea of the knowledge society. And whilst there are many learned papers describing in different ways what such a society might look like or why such a society will emerge there is little evidence of its supposed impact on labour markets. Most common is the disappearance of low and unskilled jobs, linked to growing skill shortages in high skilled employment. Yet in the UK most recent growth in employment has been in low skills, low paid jobs in the retail sector. I remember too in the late 1990s when the European industry lobby group for computers were preaching dire emergencies over the shortage of programmers, with almost apocalyptic predictions of what would happen with the year 200 bug if there were not major efforts to train newcomers to the industry. Of course that never happened either and predictions of skills shortages in software engineering persist despite the fact the UK government statistics show programmers pay falling in the last few years.

I’ve been invited to do several talks in the last year on the future of work. It is not easy. There are two lengthy reports on future skills for the UK – ‘Working Futures 2012- 2022’ and ‘The future of work: jobs and skills in 2030’, published by the UK Commission for Skills and Industry. Both are based on statistical modelling and scenario planning. As one of the reports says (I cannot remember which) “all models are wrong – it is just that some of more useful than others. Some things are relatively clear. There will be a big upturn in (mainly semi skilled) work in healthcare to deal with demographic changes in the age of the population. There will also be plenty of demand for new skilled and semi skilled workers in construction and engineering. Both are major employment sectors and replacement demand alone will result in new job openings even if they do not expand in overall numbers (many commentators seem to forget about replacement demand when looking at future employment).

But then it all starts getting difficult. Chief perhaps amongst this is possible disruptions which can waylay any amount of economic modelling. The following diagram above taken from ‘The future of work: jobs and skills in 2030’Ljubiana_june2015.001 shows possible future disruptions to the UK economy and to future jobs. One of these is the introduction of robots. With various dire reports that up to 40 per cent of jobs may disappear to robots in the next few years, I suspect we are creating another myth. Yes, robots will change patterns of employment in some industries, and web technologies enable disruptions in other areas of the economy. Yet much of the problems with such predictions lay with technological determinism – the idea that technology somehow has some life of its own and that we cannot have any says over it. At the end of the day, despite all the new technologies and the effects of globalization, there are massive policy decisions which will influence what kind of jobs there will be in the future. These include policies for education and training, inter-governmental treaties, labour market and tax policies, employment rights and so on. And such considerations should include what jobs we want to have, how they are organised, where they are and the quality of work. At the moment we seem to be involved in a race to the bottom – using the excuse of austerity – which is a conscious policy – to degrade both pay and work conditions. But it doesn’t need to be like this. Indeed, the excuses for austerity may be the biggest myth of all.

 

 

 

Reflections on Communities of Practice

March 17th, 2016 by Graham Attwell


Chahira Nouira sent me an email asking if I could make a short podcast around Communities of Practice. ” I am writing,” she said “because I thought you might have 15 min of your precious time to help me compile an audio playlist where you are the stars! For a year, I have been involved in a project funded by the EU and one of its products is a Community of Practice for Lifelong Learning: DISCUSS. My idea is to get insights from you on CoPs based on how your experience and stories”.

I have been involved – and still am – in a number of projects seeking to support the emergence of communities of practice – defined as groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly – with varying success. In the podcast I try to explain why I think some have worked an others less so.

In early days, in the late 1990s, we mainly saw the idea of Communities of Practice as an analytic tool to understand how informal learning develops in Communities of practice and how knowledge is exchanged. In a later stage we moved on to try to develop or foster Communities of Practice, using IST to support the emergence of dispersed communities.

All to often we thought we could form communities ourselves, not totally understanding the emergent nature and the ownership of CoPs. Too often also, we have conflated organisations with communities. Probably more importantly, whilst we have fused on communities, we have failed to properly understand the nature of the practices which bind together those communities. According to Wenger, a community of practice defines itself along three dimensions

  • What it is about – its joint enterprise as understood and continually renegotiated by its members.
  • How it functions ‐ mutual engagement that bind members together into a social entity.
  • What capability it has produced – the shared repertoire of communal resources (routines, sensibilities, artefacts, vocabulary, styles, etc.) that members have developed over time. (Wenger, 1998)

In seeking to support facilitation a vital prerequisite is understanding the nature of the social practices within the workplace, both through observable patterns of individual practice and through developing an overall pattern language. This includes the use of objects. Objects are necessary components of many practices – just as indispensable as bodily and mental activities. (Reckwitz, 2002). Carrying out a practice very often means using particular things in a certain way. Electronic media itself is an object which can mold social practices and enable and limit certain bodily and mental activities, certain knowledge and understanding as elements of practices (Kittler, 1985; Gumbrecht, 1988).  One approach to choosing ways to develop particular objects is to focus on what Onstenk (1997) defines as core problems: the problems and dilemmas that are central to the practice of an occupation that have significance both for individual and organisational performance.

If understanding the nature of social practices and patterns is a necessary step to developing facilitation services, it is not in itself sufficient. Further understanding is needed of how learning, particularly informal learning, takes place in the workplace and how knowledge is shared and developed. Michael Eraut (2000) points put that “much uncodified cultural knowledge is acquired informally through participation in social activities; and much is often so ‘taken for granted’ that people are unaware of its influence on their behaviour. This phenomenon is much broader in scope than the implicit learning normally associated with the concept of socialisation. In addition to the cultural practices and discourses of different professions and their specialities, one has to consider the cultural knowledge that permeates the beliefs and behaviours of their co-workers, their clients and the general public.”

Eraut attempts to codify different elements of practice:

  • Assessing clients and/or situations (sometimes briefly, sometimes involving a long process of investigation) and continuing to monitor them;
  • Deciding what, if any, action to take, both immediately and over a longer period (either individually or as a leader or member of a team);
  • Pursuing an agreed course of action, modifying, consulting and reassessing as and when necessary;
  • Metacognitive monitoring of oneself, people needing attention and the general progress of the case, problem, project or situation.

He also draws attention to the importance of what he calls mediating objects and points out that while some artifacts are used mainly during learning processes, most artifacts used for working are also used for learning. Such artefacts play an important role in structuring work and sharing information and in mediating group learning about clients or projects in progress.

In general, when seeking to support online communities, we have developed web sites and web based tools which are separate form the work process. Possibly, we should be looking instead at how we can use artifacts from work processes to support learning and knowledge exchange.

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.

ABSTRACT

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.

1. LEARNING ANALYTICS AND WORK BASED 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.

2. EMPLOYID: ASSISTING IDENTITY TRANSFORMATION THROUGH SOCIAL LEARNING IN EUROPEAN EMPLOYMENT SERVICES

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.

3. LEARNING ANALYTICS AND EMPLOYID – WHAT ARE WE TRYING TO FIND OUT?

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.

4. FROM A THEORETICAL APPROACH TO DEVELOPING TOOLS FOR LA IN PUBLIC EMPLOYMENT SERVICES

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]

5. NEXT STEPS

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.

ACKNOWLEDGEMENTS

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).

REFERENCES

[1] SoLAR(2011).Open Learning Analytics: An Integrated & Modularized Platform. WhitePaper.Society for Learning Analytics Research. Retrieved from http://solaresearch.org/OpenLearningAnalytics.pdf

[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,  https://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/

[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, http://learning-layers.eu/laforwork/

[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 http://abs.sagepub.com/content/early/2013/03/11/0002764213479364.abstract

[8] Conole G. (2014) The implciations of open practice, presentation, Slideshare, http://www.slideshare.net/GrainneConole/conole-hea-seminar

[9] Ferguson (2015) Learning Design and Learning Analytics, Presentation, Slideshare http://www.slideshare.net/R3beccaF/learning-design-and-learning-analytics-50180031

[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. http://dx.doi.org/10.1109/TKDE.2005.99

[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, http://www.tandfonline.com/doi/abs/10.1080/10494820.2014.908927

[14] MacNeill, S. (2015) The sound of learning analytics, presentation, Slideshare, http://www.slideshare.net/sheilamac/the-sound-of-learning-analytics

[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 http://nbn-resolving.de/urn:nbn:de:0114-fqs0002204

[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.http://bio.cs.washington.edu/supplements/kayee/pca.pdf

[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.

http://www.casos.cs.cmu.edu/publications/papers/CMU-CS-08-116.pdf

[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.

http://knowledge-maturing.com/concept/knowledge-maturing-phase-model

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

Confer – Three steps to consensus

February 9th, 2016 by Graham Attwell

I have written a number of post about the Learning Toolbox mobile app being developed through the Learning Layers project and of course Pekka Kamareinen has documented the development of the project in some detail on this site.

But Learning Toolbox is just one of a number of applications developed by the project and being rolled out for evaluation this spring. One which in my view holds some promise is Confer. Confer is a collaborative workflow tool, being launched under the banner of  “Confer – Three steps to consensus”. Confer provides online collaboration spaces for working groups that can be used both synchronously as well as asynchronously and supports groups in working collaboratively on a task or project; helping to keep the work focused and flowing, recording the discussions and reasoning along the way and producing a final summary output that can become the first draft of a report or recommendations.

Confer is based on research work in computer supported work and learning – for instance by Hämäläinen & Häkkinen, who say “the production of descriptive and surface-level knowledge, the difficulty in creating explanation-seeking questions, the reaching of mutual understanding among participants, and uneven participation are some of the main challenges that exist in computer-supported collaborative learning settings.”

Confer supports and scaffolds groups in working through a collaborative meaning making and decision process.

It first asks “What do we need?” by clearly describing the problem at hand including what, where, when and for whom? The second stage is to explore “What do we know?” through a brainstorming process identifying issues and collecting together relevant knowledge, resources, ideas and experience.

The third stage is decision making – “What should we do?” –  developing and describing options/solutions that will address the problem and identified issues. The end point is to agree on a recommendation.Whilst it may all sound simple in real life these processes are challenging especially with distributed groups who may only meet together face to face at limited intervals. Our research suggests that in reality one person is left alone to write up the results, thus both diminishing group expertise and often failing to develop shared meanings.

The pilot implementations of Confer start next week. But if you would be interested in trialling Confer please email me. You can find out more by visiting the Confer Zone.

The challenge for Learning Analytics: Sense making

January 28th, 2016 by Graham Attwell

https://sylviamoessinger.files.wordpress.com/2012/06/learninganalytics_chalkboard.jpg

Image: Educause

Its true Twitter can be a distraction. But it is an unparalleled  resource for new ideas and learning about things you didn’t know you wanted to learn about. This morning my attention was drawn by a Tweet linking to a interview in Times Higher Education with Todd Rose entitled “taking on the ‘averagarians’.” Todd Rose believes that “more sophisticated examples of “averagarian” fallacies – making decisions about individuals on the basis of what an idealised average person would do – are causing havoc all round.” The article suggests that this applies to higher education giving the example that “Universities assume that an average student should learn a certain amount of information in a certain amount of time. Those who are much quicker than average on 95 per cent of their modules and slower than average on 5 per cent may struggle to get a degree.”

It seems to me that this is one of the problems with Data Analytics. It may or may not matter that an individual is doing better or worse than the average in a class or that they spend more or less time reading or even worse logged on to the campus VLE. Its not that this data isn’t potentially useful but it is what sense to make of it. I’m currently editing a paper for submission to the workshop on Learning Analytics for Workplace and Professional Learning (LA for Work) at Learning Analytics and Knowledge Conference (LAK 2016) in April (I will post a copy of the paper here on Sunday). And my colleague Andreas Schmidt has contributed what I think is an important paragraph:

Supporting the 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:

  • What does an indicator or a visualization tell about how to improve learning?
  • What are the limitations of such indicators?
  • 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. 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.

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