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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.

DJ showdown: older DJs and today’s crop of Turntablists trade blows

January 28th, 2016 by Graham Attwell

Final-Logo-RadioactiveAll too often projects fail to prove sustainable. Quite simply without external funding the products and practices developed do not survive. But sometimes they take off and resonate in new ways even without a financial stimulus. So it is with RadioActive. RadioActive was a project funded under the European Commission’s Lifelong Learning project to develop the use of internet radio with unemployed people and socially disadvantaged groups. And although the funding finished over a year ago, projects partners in three countries – Germany, Portugal and the Uk are still producing radio programmes.

Today is London’s turn. At 1400 UK time, 1500 CET the Univeristy of East London present a show entitled “DJ showdown: older DJs and today’s crop of Turntablists trade blows.” Is DJing an art form? With digital tech so easily available and virtually unlimited access to MP3s via a laptop, is everyone now a DJ? And if so does that mean older people who learnt their craft through hard graft have wasted their time? Don’t all the years of physically carrying lbs of vinyl to clubs and then actually mixing records live amount to something? We examine what does it mean to be a DJ in 2016 and how it has changed over the last three decades.

We compare the different styles of mixing music ranging from Geoff Humphries who DJ’d in the house music scene of Ibiza, Rhythm Vandals (mostly playing the clubs in Leeds in the 90s) right up to newest wave of teenage Turntablists where Abrakadaniel beat mixes for us. Tracks include the Sex Pistols, Madonna/Abba, the late Lemmy from Motörhead through to Soulwax. What influence have new techniques and digital accuracy that take account of key and time signatures actually had on mixing? We hear the likes of Titancube, RiFF RAFF, Skrillex, Datsik, Brillz & LAXX and more.

You can listen live at http://listenlive.radioactive101.eu/ . And if you can’t tune in live catch up afterwards with the recording of this and other programmes at http://uk2.radioactive101.eu/broadcast/

Children in UK spend more time on the internet that in front of TV

January 27th, 2016 by Graham Attwell

The Guardian newspaper reported yesterday on a survey finding that for the first time children in the UK are spending more time on the internet than in front of the TV.

Research firm Childwise found that on average five- to 15-year-olds were spending three hours a day using the internet, compared to 2.1 hours watching TV.

While time spent watching television has been in decline for some years, time online has seen a huge surge according to the research, up 50% from two hours last year.

However, there are  some problems with the survey results. The research, which is based on an online survey of more than 2,000 children, did not distinguish between TV-like services on the internet, such as Netflix and iPlayer, and other forms of browsing such as Facebook, meaning it is unclear whether children are merely watching shows in different ways.

However, says the Guardian “the report says that YouTube has taken “centre stage in children’s lives” with half accessing it every day and almost all using it at least occasionally.

The majority of children who use YouTube visit the site to access music videos (58%), while around half watch “funny content” and a third say they watch gaming content, vlogs, TV programmes or “how to” videos.”

The survey also reported that time spent reading books for pleasure has declined from an hour a day on average in 2012 to just over half an hour on average this year. However, once more this does not include time reading books on computers.

I am not sure that raw figures of time spent watching TV versus time spent on the internet, be it computers, tablets or mobiles is the real story, although it might be of concern to advertising executives. More interesting would be to know more about patterns of use of computers, what levels of interaction there are with others and the degree to which computers are used actively or creatively compared to the passive entertainment which marked most television viewing.

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.

Yishay Mor talks about Design Patterns

January 14th, 2016 by Graham Attwell

At Online Educa Berlin 2015, I had the opportunity to interview Yishay Mor (see podcast below). I was keen to talk to him as he has been one of the people pushing the idea of Design Patterns in technology enhanced learning. And in both the two EU research projects in which I am involved, EmployID and Learning Layers, we are adopting patterns as a design tool or methodology. Both projects from their inception were committed to user centred design. But that left major issues of how to do it. It is not just a matter of getting a group of potential users together and talking with them. We need a language to structure conversations and a language which can describe practice. We have experimented with Personas which I suppose can be described as ideal types. However, all too often the persona ceased to correspond to any reality – or contained a mix of practices from multiple people – rendering them extremely problematic for design purposes.

Design narratives, design patterns and design scenarios seem to offer a potentially richer process for designing for learning, furthermore they may have considerable value in describing innovations in technology. Despite releasing applications as open source, they fail to be picked up on – especially for occupational learning, as the potential uses are opaque.

The following notes are taken from Yishay Mor and Steven Warburton’s paper, ‘Assessing the value of design narratives, patterns and scenarios in scaffolding co-design processes in the domain of technology enhanced learning.

Design narratives provide an account of the history and evolution of a design over time, including the research context, the tools and activities designed, and the results of users’ interactions with these

Design narratives offer thick descriptions of innovations, but they are often too specific to lend themselves to efficient transfer to novel challenges. Design patterns fill this gap by offering a “grounded abstraction” of design knowledge distilled from design narratives. Design patterns originate in the work of Christopher Alexander and his colleagues in the theory of architecture (Alexander, 1977).

A design pattern describes a recurring problem, or design challenge, the characteristics of the context in which it occurs, and a possible method of solution. Patterns are organized into coherent systems called pattern languages where patterns are related to each other. The core of a design pattern can be seen as a local functional statement: “for problem P, under circumstances C, solution S has been known to work.

There are many different ways of describing patterns. In EmployID, reflecting its status as a research project we have adopted the following template:

Problem: What is the learning problem that has been addressed? This encompasses a sufficiently generalized version of a learning scenario

Analysis: Interpretation of the problem from a theory perspective

Context: What are the relevant contextual factors that determine if the proposed solution is actually (and maybe allegedly) successfully applicable?

Solution: What is the (socio-)technical solution?

Evidence: Accumulated evidence that the solution is a solution to the problem when the contextual conditions are met, e.g., examples in a specific context, but also feedback from external stakeholders that problem-solution pairs appear applicable in other contexts.

PLE Special Edition

January 13th, 2016 by Graham Attwell

OK – the ed-tech world moves on to its latest craze. But Personal Learning Environments have not gone away as the new call for papers for the PLE Conference 2015 (Special Edition) makes clear:

Since the emergence of the term Personal Learning Environments (PLE) in the scientific discussion around the year 2004 in Oxford, PLE have become a field of research that has opened up great opportunities for reflection on almost all important aspects of education and learning with technology at all levels; from the study and development of tools, interaction processes among participants, cognitive mechanisms of individual learning, learning in groups and networked learning; life long learning; personal learning networks; even organizational learning environments, and so on.

In these years, the discussion has also transcended the traditional boundaries of academia and has been amplified in both the forms and contexts in which it takes place. The communities created around the concept and practices of PLE, have been responsive not only in the reflections on the realities concerning to learning, but also to the way in which these reflections are made.

Therefore, in this particular special issue supported by the PLE Conference community and its reflections during 2015, we would like to have a compilation of the current state of the field, papers that allow readers to have a vision of what are the most topical ideas and practices around the PLE nowadays, but with a clear vision of where the analysis is headed.

Find the full details here.

Thinking about Practice and Design

January 13th, 2016 by Graham Attwell

Sometimes writing reports for European projects can be a chore. Long, boring and nobody reads them. At the moment I am writing sections for the EmployID project second annual report. Instead of writing individual work package reports, as is the normal convention, we are writing a single report in the form of a book. And that provides more incentive to get it right. Plus the sections I am writing are all difficult – social learning, Learning Analytics and Labour Market Information tools, but are making me think. So I am quite enjoying it – I think. This last two weeks I have been working on design – or more specifically design for learning. How can we develop designs for tools to support informal learning in public service organisations. I am going to publish here a short series of posts outlining the way I am thinking. I am not sure if this stuff is write but would appreciate any feedback. The first post, today is about practice. Tomorrow I iwll look at the idea of Design Patterns and follow that up on Friday with a draft of a design pattern for Labour market Information tools.

Social Learning

EmployID 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 f 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” (Bandura, 1977). 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.

Proposals and initiatives to utilise new technology for learning and professional development in organisations is hardly new. However, a critical review of the way information technologies are being used for workplace learning (Kraiger, 2008) concluded that most solutions are targeted towards a learning model based on the idea of direct instruction. Technology Enhanced Learning initiatives tend to be based upon a traditional business training model transferred from face to face interactions to onscreen interactions, but retaining the standard trainer / learner relationship and a reliance on formal and to some extent standardised course material and curricula.

Research suggests that much learning that takes place in the workplace and through work processes, is multi episodic, is often informal, is problem based and takes place on a just in time basis (Attwell 2007; Hart, 2011). Rather than a reliance on formal or designated trainers, much training and learning involves the passing on of skills and knowledge from skilled workers (Attwell and Baumgartl, 2009). In other words, learning is both highly individualized and heavily integrated with contextual work practices and is inherently social in its nature.

To succeed in supporting identity transformation it is not enough merely to develop or deploy technologies which support training and information transmission. Rather, EmployID needs to develop approaches and pedagogies which can support social facilitation services within PES organisations and which empower individuals to engage in peer learning and facilitation around their own practices.

Although there is much research around the use of technology for learning, far less attention has been paid to informal learning and facilitation processes in the workplace. Research around social practice has largely remained the preserve of social science with different approaches based on structuralism, phenomenology and intersubjectivism amongst others. In his paper on theories of social practice, Reckwitz (2002) draws attention to the dual meaning of the English word practice in German.

“Practice’ (Praxis) in the singular represents merely an emphatic term to describe the whole of human action (in contrast to ‘theory’ and mere thinking). ‘Practices’ in the sense of the theory of social practices, however, is something else. A ‘practice’ (Praktik) is a routinized type of behaviour which consists of several elements, interconnected to one other: forms of bodily activities, forms of mental activities, ‘things’ and their use, a background know- ledge in the form of understanding, know-how, states of emotion and motivational knowledge. A practice – a way of cooking, of consuming, of working, of investigating, of taking care of oneself or of others, etc. – forms so to speak a ‘block’ whose existence necessarily depends on the existence and specific inter-connectedness of these elements, and which cannot be reduced to any one of these single elements.

Likewise, a practice represents a pattern which can be filled out by a multitude of single and often unique actions reproducing the practice (a certain way of consuming goods can be filled out by plenty of actual acts of consumption). The single individual – as a bodily and mental agent – then acts as the ‘carrier’ (Träger) of a practice – and, in fact, of many different practices which need not be coordinated with one another. Thus, she or he is not only a carrier of patterns of bodily behaviour, but also of certain routinized ways of understanding, knowing how and desiring. (pp249-250)”

In this understanding knowledge is more complex than ‘knowing that’. It embraces ways of understanding, knowing how, ways of wanting and of feeling that are linked to each other within a practice.

In seeking to support facilitation within public employment services 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:

1.     Assessing clients and/or situations (sometimes briefly, sometimes involving a long process of investigation) and continuing to monitor them;

2.     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);

3.     Pursuing an agreed course of action, modifying, consulting and reassessing as and when necessary;

4.     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 mediat9ing group learning about clients or projects in progress.

Among informal learning processes that Eraut lists are participation in group processes, consultations, problem solving, trying things out and working with clients. Working alongside others is important in allowing “people to observe and listen to others at work and to participate in activities; and hence to learn some new practices and new perspectives, to become aware of different kinds of knowledge and expertise, and to gain some sense of other people’s tacit knowledge.”

Tackling challenging tasks and roles requires on-the job learning and, if well- supported and successful, leads to increased motivation and confidence.

 

According to De Laat (2012) informal learning in the workplace is often described as observing how others do things, asking questions, trial and error, sharing stories with others and casual conversation (Marsick and Watkins, 1990). Boud and Hager (2012) argue that learning is a normal part of working and professional development should be placed in a social context where professionals work and learn together, changing and innovating both their professional practice as well as their professional identity.

De Laat (2012) argues that we need to find a new balance between formal and informal learning and provide opportunities for what Fuller and Unwin (2003) call expansive ‐ as opposed to restrictive learning ‐ through developing an organisational culture that values and supports learning and by so doing, opens doors to various opportunities for professional development. Informal professional development through engagement in social learning spaces can enable participation, construction and ‘becoming’ (De Laat, 2012).

Lave and Wenger (1991) also stress the importance of both practice and the social nature of learning in their conception of Communities of Practice.  Interestingly for them, collective learning results in practices that reflect both the pursuit of our enterprises and the attendant social relations. “These practices are thus the property of a kind of community created over time by the sustained pursuit of a shared enterprise. It makes sense, therefore to call these kinds of communities communities of practice.”

“Communities of Practice are important to the functioning of any organisations, but they become crucial to those that recognise knowledge as a key asset. An effective organisation comprises a constellation of interconnected CoPs, each dealing with specific aspects of the company’s competency, from the peculiarities of a long standing client, to manufacturing safety, to esoteric technical inventions. Knowledge is created, shared. organised, revised, and passed on within and among these communities.” (Wenger, 1998).

Connecting people in parallel, across disciplines, roles and departments of the business, is fundamentally different from connecting people in project teams or interest groups. Although the nature and composition of these communities varies members are brought together by joining in common activities and by ‘what they have learned through their mutual engagement in these activities’

According to Wenger (1998), 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)

A number of issues emerge in studies of attempts to develop communities of practice. One is a tendency to build a platform and ‘declare’ the existence of a community of practice, rather than supporting emergence and therefore ownership. The second is to fail to recognise that such a process of emergence is continuous and ongoing. A third is to conflate organisational structures with communities and to focus on the organisational nature of the community rather than the routines and artefacts that define the capability of practices.

In a similar way social learning is not something which can be done to people. Instead an approach to social learning has to be based on facilitation of social learning processes with organisations and within Communities of Practice. Such facilitation needs to relate to the social practices of people. Murphy (2004) has conceptualized collaboration as a continuum of processes, and developed an instrument with six stages for the purpose of identifying and measuring online asynchronous collaboration: “(1) social presence (2) articulating individual perspectives (3) accommodating or reflecting the perspectives of others (4) co-constructing shared perspectives and meanings (5) building shared goals and purposes, and (6) producing shared artefacts.” However, these six stages can also serve as a template for social learning processes and inform the work of EmployID in developing tools which can facilitate social learning.

References

Attwell, G. (ed.) (2007). Searching, Lurking and the Zone of Proximal Development. E-Learning in Small and Medium Enterprises in Europe, Vol.5, Navreme Publications, Vienna

Attwell, G. & Baumgartl, B. (Eds.) (2009): Creating Learning Spaces: Training and Professional Development for Trainers. Vol.9, Navreme Publications, Vienna

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

Boud, D. & Hager, P. (2012). Re-thinking continuing professional development through changing metaphors and location in professional practice. Studies in Continuing Education, 34(1),17-30

De Laat, M. (2012) Enabling professional development networks: How connected are you?, Open University of the Netherlands, Hagen

Eraut, M. (2000) Non-formal learning and tacit knowledge in professional work, British Journal of Educational Psychology (2000), 70, 113–136

Fuller, A., & Unwin, L. (2003). Learning as apprentices in the contemporary UK workplace: Creating and managing expansive and restrictive participation. Journal of Education and Work, 16(4), 407-42

Gumbrecht, H. U. (Ed.) (1988) Materialität der Kommunikation. Frankfurt/Main: Suhrkamp.

Hart, J. (2011) Learning is more than Social Learning, http://www.elearningcouncil.com/content/social-media-learning-more-social- learning-jane-hart, retrieved 5 July, 2012

Kittler, F. (1985) Aufschreibesysteme 1800/1900. München: Fink.

Kraiger, K. (2008). Transforming Our Models of Learning and Development: Web- Based Instruction as Enabler of Third-Generation Instruction. Industrial and Organizational Psychology, 1(4), 454-467. doi:10.1111/j.1754-9434.2008.00086.x

Lave, J. and Wenger, E. (1991) Situated Learning. Legitimate peripheral participation, Cambridge: University of Cambridge Press,

Marsick, V. J., & Watkins, K. (1990).Informal and Incidental Learning in the Workplace.London: Routledge

Murphy, E. (2004). Recognizing and promoting collaboration in an online asynchronous discussion. British Journal of Educational Technology, 35(4), 421-431.

Onstenk, J. (1997) Lerend leren werken: Brede vakbekwaamheid en de integratie van leren, werken en innovere

Reckwitz A (2002) Toward a Theory of Social Practices, European Journal of Social Theory 2002 5: 243

Tennant, M. (1999) ‘Is learning transferable?’ in D. Boud and J. Garrick (eds.) Understanding Learning at Work, London: Routledge.

Wenger, E. (1999), Communities of Practice. Learning, meaning and identity, Cambridge: Cambridge University Press

Wenger, E. (1998) ‘Communities of Practice. Learning as a social system’, Systems Thinker,

 

Stagnation or innovation in Technology Enhanced Learning?

January 12th, 2016 by Graham Attwell

Just a quick note following up on my blog of yesterday noting the lack of new ideas in the exhibition at Online Educa Berlin. Today I read an interesting article entitled “Caputalism: Will Capitalism Die?” by Robert Misik on the Social Europe blog. Most of the article, as the title implies is given over to an analysis of the lack of growth and “secular stagnation” in western economies.

Misik says that “despite superficial impressions, the past 15 years may have produced practically no more genuinely productive innovations.” He quotes the economist Robert J Gordon who says:“Invention since 2000 has centered on entertainment and communication devices that are smaller, smarter, and more capable, but do not fundamentally change labour productivity or the standard of living in the way that electric light, motor cars, or indoor plumbing changed it.”

And that seems to sum up much of the developments in Technology Enhanced Learning. Whilst in the 1990s and the first years of this century there was something of an explosion in innovative uses of technology for learning through mainly the development of Virtual Learning Environments, since then genuine innovation has stalled, as least through the ed tech industry. Games based learning, Learning Analytics, mobile learning, MOOCs are all interesting but they do not, to paraphrase Gordon, fundamentally change education and learning, still less pedagogy. As Phil Hill says: “Didn’t we have bigger dreams for instructional technology?”

Misik speculates on the slow emergence of a new economy in which “more decentralized, self-managed firms, co-operatives and initiatives play a gradually more important role – so that, in the end, a mixed economy emerges composed of private companies, state enterprises and co-operatives and alternative economic bodies.” And that may be the way forward to for Technology Enhanced Learning, where the behemoths of the Ed Tech world play a lesser role, where governments continue to invest in innovation in teaching and learning with technology in education, where the importance of state involvement in education is recognised and where smaller more agile private sector enterprises become partners in developing new initiatives and pedagogic approaches to learning. Its nice to be optimistic!

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    They say: “In this first webinar we will explore new trends in European MOOCs. Rosanna de Rosa, from UNINA, will present the philosophy and challenges behind the EMMA EU project and MOOC platform developed with the idea of accommodating diversity through multilingualism. Darco Jansen, from EADTU (European Association of Distance Teaching Universities), will talk about Europe’s response to MOOC opportunities. His presentation will highlight the main difference with the U.S. and discuss the consequences for didactical and pedagogical approaches regarding the different contexts.


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