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Short talk on Future of Work and Skills

Last week I gave a short talk on the future of work and skills at the 10th anniversary of People Per Hour online freelancing platform in London. Here is the text of the talk.

Good evening and happy birthday, People per Hour! Thank you very much for the opportunity to join the celebration!

I was asked to share brief reflections on the future of work and learning. So I’d like to offer a few general observations about the changing nature of work, then outline a few specific skills and attributes that I think will be crucial moving forward.

Changing nature of work

I want to begin by suggesting that much of the current discourse on the future of work –that intelligent machines will destroy jobs causing mass unemployment and societal upheaval –  seems ahistorical. Historically, introduction of new technologies has always destroyed some jobs and created others. There doesn’t seem to be a compelling reason to expect that the current cycle of technology development will be radically different in terms of this fundamental pattern.

Secondly, just because jobs and work tasks can be automated doesn’t mean that they will be. Technology is not exogenous to our society. It is part of an overall socio-technical system of the society. Historically, the deployment of technology has been enabled as well as constrained by broader forces – economic, legal, ethical, societal and political considerations. We don’t know just exactly how these broader factors will play out and how they may play out differently in different countries.

Thirdly, scholars studying the impact of artificial intelligence have pointed out that currently few if any intelligent machines and systems are truly autonomous, but that rather the role of humans is often obscured in these systems.  Madelene Elish, who is a cultural anthropologist at Columbia University, argued that “when we examine autonomous systems we must look not to the erasures of the human, but to the ways in which we, as humans, are newly implicated”…

Information scientists Bonnie Nardi and Hamid Ekbia coined the term ‘heteromation’ to denote the paradigm of work premised on the division of labour between machines and humans. They argue that unlike the technologies of automation, the aim of which is “to disallow human intervention at nearly all points in the system”, the technologies of heteromation “push critical tasks to end users as indispensable mediators, drawing the humans back into the computational fold”. This sorts of heteromative systems are currently in evidence, from Amazon Mechanical Turk to factory floors in Germany and China where humans increasingly collaborate with robots in our production systems. It seems likely that at least in the medium-term, it is such heteromative arrangements rather than the pure automation paradigm that will predominate.

The historian Yuval Harari has pointed out that “not just the idea of ‘a job for life’, but even the idea of a ‘profession for life’ will soon come to seem antediluvian”. Harari’s argument suggests that we will have to be flexible, not just in terms of our knowledge and skills, but also sector and the location where we work, our professional identity and be prepared to drastically reorient ourselves several times within our careers.

This takes me to the future of learning and skills…

Future of learning and skills

I’d like to suggest to you that, as we undergo a range of these horizontal and vertical reorientations implied in Harari’s argument, the ability to actively initiate rather than only endure these transitions will become a crucial mindset.  Organisational scholars Lynda Gratton and Andrew Scott pointed out that people who are successful in undergoing transitions share at least three important abilities. First, they are self-reliant and possess the necessary self-knowledge to understand who we they are and what they may be in future. Second, they have dynamic and heterogeneous networks to provide them with role models of what they could be and how to become it. And third, they are risk takers, they have tolerance for uncertainty, and openness to experience “to act their way into change”.

I’d argue that presently our public discourse and policy landscape is overly focused on the formal, institutionalised forms of education and training. Limited recognition and value is given to self-directed and self-initiated forms of learning that people undertake throughout their lives, individually or in cooperation with others. The problem is that the educational institutions we have come to rely on for our learning and training are very conservative and slow. And it is questionable that they will be able to provide the rapid and dynamic reskilling and reorientation that individuals will require.

So I’d like to suggest that moving forward the most important attribute that individuals must acquire is the ability to self-regulate and self-direct their learning. This means, being strategic and dynamic in identifying our learning goals and strategies, being proactive in seeking feedback on our learning and work, continuously studying the market to understand and identify the changing skill requirements, strengthening our personal self-efficacy, being self-reflective and able to dynamically change our learning strategies when these are not working. These attributes will be increasingly required of everyone, not just those in managerial or highly-skilled jobs. These individual attributes have always been important psychologically, but I’d argue that they have now become crucially important economically as well.

My last point is that moving forward some radically new skills will also be required. One of these is for us to learn to cooperate effectively with the intelligent machines in our increasingly heteromative workplaces. The emergent forms of algorithmic management of labour necessitate the ability to work productively in a supervisory relationship with a machine. A key question I would like to ask is this: in a workplace where your boss is a machine, where algorithms allocate and dynamically price work, algorithms record and evaluate the quality of the output, algorithms assess workers’ performance and recommend learning activities to improve performance what are the skills, attributes and dispositions needed to work productively with these machines and how can people develop these capabilities?  Presently, our policy landscape appears to overlook these fundamentally new skill requirements altogether and our educational and vocational systems are doing little to prepare people for this new reality.

Let me conclude by observing that to succeed in the future of work, we have a greater need for personal autonomy, self-reliance and self-direction. At least for some time to come there will be no accepted code of rules to absolve us of the challenge of individual decision making in our work and in our learning. Thank you.

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Using life course perspective to understand learning practices within crowdwork

I am pleased to have an abstract accepted for the ‘Research Methods for Digital Work: Innovative Methods for Studying Distribute and Multi-modal Working Practices’ conference organised by the Institute of Advanced Studies at the University of Surrey.

In the abstract, Heather Hofmeister and I outline how the Life Course perspective could help advance research into digital work and particularly the study of learning practices within crowdwork.  To my knowledge, this is the first attempt to apply the Life Course perspective in crowdwork research. If you know of other research where the Life Course perspective has been used to study crowdwork or other gig-economy practices please get in touch.

Abstract: Using life course perspective to understand learning practices within crowdwork

Anoush Margaryan and Heather Hofmeister, Department of Sociology, Goethe University Frankfurt 

Background

The unfolding digitalisation of our society has stimulated the development of new types of work practices termed ‘virtual work’ (Huws, 2014). These emerging digitally-mediated work practices challenge traditional patterns of individual agency, organisation, power, responsibility and learning (Littlejohn and Margaryan, 2014). Working under conditions of precarity, digital transformation and changing patterns of agency, workers increasingly have to initiate and regulate their own learning. As the nature of work evolves, understanding how workers learn within these new work practices becomes increasingly important.

One type of virtual work is crowdwork – a form of labour in which a large group of people are brought together within Internet-based platforms for the purpose of performing a task. These platforms act as intermediaries between clients/task requesters and workers, helping oversee the definition, submission, acceptance and payment for the work done (Kuek et al, 2015). Examples are Amazon Mechanical Turk, Upwork and CrowdFlower.  In this highly distributed and fragmented type of work, where workers may not have access to the learning opportunities available within traditional employment (eg training or access to experienced colleagues), how do crowdworkers go about managing their learning? What strategies do crowdworkers use to identify their learning needs, source knowledge, and find others to learn with and from?

In crowdwork research, three key methods have been used – questionnaire survey, interview, and ethnography- all focusing on crowdworkers (Gray et al, 2016; Ipeirotis, 2010; Martin et al, 2016). The perspectives of other key stakeholders e.g. platform providers and task requesters/clients have been overlooked. Also, workers’ experiences have been examined as snapshots rather than being contextualised historically and developmentally.

This paper argues that essential to understanding the learning practices within crowdwork is to analyse both the individual and the historical-environmental factors impacting crowdworkers’ learning. Crowdwork occurs largely online, although it is plausible that crowdworkers’ learning activities span the online and offline realm. Methodological approaches that bridge sociological, psychological, individual, collective, online, offline, and temporal processes and practices of learning within crowdwork are needed.

Life course perspective

We propose the life course perspective as an analytical framework to facilitate a nuanced, contextualised analysis of crowdworkers’ learning (Elder and Giele, 2009; Hofmeister, 2015; Levy, 2013). The life course is an interdisciplinary perspective drawing on sociology, psychology, anthropology, history and biology to help understand human development across the life span (Mortimer and Shanahan, 2004). The life course perspective stresses the importance of the socio-cultural environment in explaining individual behaviour and life history. It focuses on the interplay of the individual, their setting, and the dynamic processes of change individuals undergo within these settings.

Four key elements of the life course perspective would help analyse crowdworkers’ learning: agency, context, linked lives, and timing. Agency refers to an individual’s motives and goals, and the self-regulated activities undertaken to fulfil them (Elder, 1994). Context refers to the setting in which each individual acts, comprising psychological, social, cultural, organisational, technological and physical dimensions, as well as the temporally-constituted patterns that emerge from the interplay between these diverse contexts (Blossfeld, 2009; O’Rand, 2009). Linked lives denotes the interrelations between individuals in their contexts (Moen and Hernandez, 2009). Timing refers to the sequencing of events and pathways of personal activities individuals engage in to reach their goals (Viry et al, 2013).

Quantitative and qualitative methods have been applied in Life Course research (Elder and Giele, 2009). For example, Laub and Sampson (1998) discuss a longitudinal study of juvenile delinquency where data on social, psychological and biological characteristics, family life, school performance, work experience were collected from multiple sources, several points of view and at separate times. They show how the merging of quantitative and qualitative data provides important cues for explaining continuity and change in human behaviour.

Applying life course perspective to crowdworkers’ learning    

The project this paper is based on examines learning strategies and activities, personal motivations, goals, agency beliefs and pathways underpinning crowdworkers’ learning, and the individual and environmental factors impacting upon their learning. Data are collected from two platforms: CrowdFlower and Upwork.

 Several methods are combined to help generate rich descriptions of crowdworkers’ learning practices (Johnson et al., 2004). Crowdworkers’ self-regulatory learning strategies and learning activities are scoped using the Self-Regulated Learning at Work Questionnaire, SRLWQ (Fontana et al., 2015). The survey is supplemented by biographical interviews to ascertain crowdworkers’ professional trajectories and learning pathways, educational and work experiences, current and desired skills, learning goals and motivations to engage in crowdwork and learning.  The interviews are combined with field visits to conduct observations of crowdworkers’ local contexts and to collect data on specific SRL strategies in situ using SRL microanalysis protocols (Cleary, 2011).  Online ethnography is carried out within discussion fora and social networks used by crowdworkers and clients/task requesters to identify learning activities. To contextualise crowdworkers’ perspectives, representatives of crowdwork platforms and selected task requesters are interviewed and training and development provisions offered by the platforms are scoped. Table 1 illustrates how these methods help elucidate the key components of the life course applied to crowdworkers’ learning.

Table 1.  Mapping of methods and the life course framework

Life course components Methods
Human agency

·       Motives to engage in crowdwork

·       Learning and performance goals

·       Career aspirations

·       SRL strategies

·       Learning activities

·       Existing knowledge and skills

 

·       Biographical interviews with crowdworkers

·       Analysis of online fora

·       SRLWQ

·       SRL microanalysis

·       Experience sampling methods (eg diary or tracking devices)

Context

·       Design of crowdwork platforms

·       Task design

·       Physical environment

·       Local infrastructure

·       Local culture

·       Other work/professional commitments

·       Education and training

·       Previous work experiences

·       Local economic conditions, employment and regulatory regimes

·       Training and development provision by the platforms

·       Review of platforms

·       Scoping of sample work tasks

·       Interviews with platform providers and clients

·       Interviews with crowdworkers

·       Interviews with policymakers (trade unions, labour organisations, politicians)

·       Ethnographic observation

·       Document review

·       Analysis of artefacts

Linked lives

·       Family, friends, neighbours

·       Professional networks in and outside crowdwork

·       Clients and employers

·       Online communities

·       Client networks

 ·       Biographical interviews

·       Interviews with platforms and clients

·       Opportunistic interviews with family members/friends

·       Social network analysis

Timing

·       Education

·       Workplace

·       Retirement

·       Disability

·       Immigration

·       Loss of job

·       Decision to freelance

·       Family events

·       Significant other prior events/experiences

·       Biographical interviews

The data collection is in early stages and specific examples of data capture and analytic techniques will be demonstrated at the conference. Opportunities and challenges in mixing methods to study crowdworkers’ learning will be discussed.

The paper contributes an interdisciplinary methodological perspective drawing on Sociology, Learning Sciences, Psychology, Internet Studies and HCI offering insight into how people work and learn within crowdwork and how crowdwork may be shaped to foster learning. 

References

Blossfeld, H. P. (2009). Comparative Life Course research. In G. H. Elder, & J. Z. Giele (Eds.), The craft of Life Course research (pp. 280-306). New York: Guilford Press.

Cleary, T. (2011). Emergence of self-regulated learning microanalysis. In Zimmerman, B., & Schunk, D. (Eds.), Handbook of self-regulation of learning and performance. London: Routledge.

Elder, G. H. (1994). Time, human agency, and social change. Social Psychology Quarterly, 57(1), 4-15.

Elder, G. H., & Giele, J. Z. (Eds.). (2009). The craft of Life Course research. New York: Guilford Press.

Fontana, P. et al (2015). Measuring self-regulated learning in the workplace. International Journal of Training and Development, 19(1), 32-52.

Gray, M. et al (2016). The crowd is a collaborative network. In Proceedings of CSCW 2016 Conference (pp. 134-147). San Francisco: ACM.

Hofmeister, H. (2010). Life Course. In S. Immerfall, & G. Therborn (Eds.), Handbook of European societies (pp. 385-411). New York: Springer.

Hofmeister, H. (2015). Individualisation of the life course. International Social Science Journal, 64(213), 279-290.

Huws, U. (2014). Labour in the global digital economy. New York: Monthly Review Press.

Ipeirotis, P. (2010). Demographics of Mechanical Turk. http://www.ipeirotis.com/wp-content/uploads/2012/02/CeDER-10-01.pdf

Johnson, R., & Onwuegbuzie, A. (2004). Mixed methods research. Educational Researcher, 33(7), 14-26.

Kuek, S. C., et al. (2015). The global opportunity in online outsourcing. Washington, DC: World Bank.

Laub, J. H., & Sampson, R. J. (1998). Integrating quantitative and qualitative data. In J. Z. Giele, & G. H. Elder, Jr. (Eds.), Methods of Life Course research (pp. 213-230). Thousand Oaks, CA: Sage.

Laub, J. H. et al (1998). Trajectories of change in criminal offending. American Sociological Review, 63(2), 225-238.

Levy, R. (2013). Life Course analysis. In R. Levy, & E. D. Widmer (Eds.), Gendered life courses between standardization and individualization (pp. 315-338). Zürich: LIT.

Littlejohn, A., & Margaryan, A. (2014). Technology-enhanced professional learning. London: Routledge.

Martin, D., et al. (2016). Turking in a global labour market. Computer-Supported Cooperative Work, 25(1), 39-77.

Moen, P., & Hernandez, E. (2009). Social convoys. In Elder, G. H., & Giele, J. Z. (Eds.), The craft of Life Course research (pp. 258-79). New York: Guilford Press.

Mortimer, J.T., & Shanahan, M.J. (2004) (Eds.). Handbook of the life course. New York: Springer.

O’Rand, A. M. (2009). Cumulative processes in the Life Course. In G. H. Elder, & J. Z. Giele (Eds.), The craft of Life Course research (pp. 121-140). New York: Guilford Press.

Viry, G. et al (2013). Residential trajectories in the early life course and their effects. In Levy, R., & Widmer, E. D. (Eds.), Gendered life courses between standardization and Individualization (pp. 141-160). Zürich: LIT.

 

R&D Agenda for Learning from Incidents

I am delighted our paper ‘Research and Development Agenda for Learning from Incidents’ (co-authored with Allison Littlejohn and Neville Stanton) has been accepted for publication in Safety Science.

Get in touch if you’d like a pre-print copy.

Abstract: This paper outlines a research and development agenda for the nascent field of Learning from Incidents (LFI). Effective, deep and lasting learning from incidents is critical for the safety of employees, the general public and environmental protection. The paper is an output of an international seminar series ‘Interdisciplinary Perspectives on Learning from Incidents’ funded by the UK Economic and Social Research Council (ESRC) in 2013-2016 http://lfiseminars.ning.com/ The seminar series brought together academics, practitioners and policymakers from a range of disciplines and sectors to advance the theory, methodology, organisational practice and policy in LFI. Drawing on a range of disciplinary and sectoral perspectives, as well as on input from practitioners and policymakers, this paper lays out four key research and development challenges: defining LFI; measuring LFI; levels and factors of LFI; and strengthening research-practice nexus in LFI.

Reference (incomplete): Margaryan, A., Littlejohn, A., & Stanton, N. (in press). Research and development agenda for Learning from Incidents. Safety Science.

 

Learning within crowdwork platforms

My paper on crowdworkers’ learning within microwork and online freelancing platforms has been accepted at Internet, Policy and Politics 2016 Conference organised by Oxford Internet Institute. I’m very much looking forward to the conference.

Abstract: 

This paper reports findings of a survey exploring how crowdworkers develop their knowledge and skills in the course of their work on digital platforms. The focus is on informal learning initiated and self-regulated by crowdworkers: engaging in challenging tasks; studying professional literature/online resources; sharing knowledge and collaborating with others. The survey was run within two platforms representing two types of crowdwork – microwork (CrowdFlower) and online freelancing (Upwork). The survey uncovered evidence for considerable individual and social learning activity within both types of crowdwork. Findings suggest that both microwork and online freelancing are learning-intensive and both groups of workers are learning-oriented and self-regulated. Crowdwork is a growing form of employment in developed and developing countries. Improved understanding of learning practices within crowdwork would inform the design of crowdwork platforms; empower crowdworkers to direct their own learning and work; and help platforms, employers, and policymakers enhance the learning potential of crowdwork.

 

Reference: Margaryan, A. (22 September, 2016). Understanding crowdworkers’ learning practices. Paper presented at Internet, Policy and Politics 2016 Conference, Oxford Internet Institute, University of Oxford, UK.  [Online] http://ipp.oii.ox.ac.uk/sites/ipp/files/documents/FullPaper-CrowdworkerLearning-MargaryanForIPP-100816%281%29.pdf

 

Career lessons from Marissa Mayer

I admire Marissa Mayer and always enjoy hearing her interviews and talks.  I admire her for her brightness, work ethic, the combination of shyness and confidence, of geekiness and femininity, and for her professional achievements.

I listened to an interview she gave at Cleveland Clinic Ideas for Tomorrow event earlier this year.  In this interview she talks about her work at Yahoo and Google, her career choices and the rationale underpinning these. She concludes by outlining the lessons she has learned. Here is a summary of career lessons from Marissa Mayer:

  1. Often there is not a good or a right choice, but there are many good choices – it’s about picking well and then committing to it.
  2. Surround yourself with the smartest people you can to challenge you and push you to the next level.
  3. Go for things you are not yet ready to do – you will either learn that you are better than you thought or that you fall short of where you want to be – both outcomes are a huge learning opportunity.
  4. When choosing a job, go for an environment you’re comfortable in – you want to be somewhere where you are surrounded by like-minded people, where you can realise your full potential, where you can have your voice, where you can influence. It may seem that this point contradicts 2 and 3 above but it doesn’t – what Mayer is talking about here is not seeking a non-challenging environment but one where there is a mutual cultural and value fit between you and the organisation.  The metaphor she used to explain this is: ‘when doing sports you should really push yourself, but wear comfortable clothes’.
  5. And the most important advice in my view – Work somewhere where there is someone in the leadership who will believe in you and who will invest in you so that you can take your work to the next level and learn something new constantly.

 

 

 

Call for Papers, Safety Science Special Issue on Learning from Incidents

Updated on 5 January 2016:

The timeline for our Safety Science special issue on Learning from Incidents has been extended as follows:

31 March 2016 – Submission deadline for papers
1 July 2016 – Authors receive reviewers’ comments
31 September – Revised manuscripts submitted
31 December – Authors receive reviewers’ comments on revised manuscripts
1 February – Authors receive decision on manuscripts
31 March 2017 – Editorial and order of manuscripts passed onto journal administrator
End of Q3 – special issue published

 

Neville Stanton, Allison Littlejohn and I are co-editing a special issue on Learning from Incident to be published in Safety Science.

Papers are due by 31 March 2016. Further details and instructions are below.

Do let us know if you are interested in submitting a paper. Help in circulating this call would be much appreciated.  We look forward to receiving submissions.

Call for papers: Special issue of Safety Science on Learning from Incidents

Guest Editors

Professor Neville Stanton, University of Southampton n.stanton@soton.ac.uk

Dr Anoush Margaryan, Glasgow Caledonian University anoush.margaryan@gcu.ac.uk

Professor Allison Littlejohn, Open University Allison.littlejohn@open.ac.uk

Overview

The ability to learn from incidents it essential for safety in all organisations, industries, regulatory bodies and policy makers. Safety Science has a long history of innovations in theory, methodology, science and application. For example, accident causation models that first emerged in the early 1900s have since evolved to consider entire systems and emergent properties (e.g. Heinrich, 1931; Leveson, 2004; Perrow, 1984; Rasmussen, 1997; Reason, 1990). Similarly, methodologies have moved from focussing on tasks (Taylor, 1911) to entire systems and the constraints shaping behaviour (e.g. Vicente, 1999). However Learning from Incidents is yet to embrace theories and methods from the learning sciences. A new repertoire of theories, methods and instruments evolved from interdisciplinary perspectives is needed to learn from incidents effectively.

The aim of this special issue is to provide researchers and practitioners with an opportunity to present and discuss contemporary, forecasted, and required paradigm shifts to learn from incidents. We welcome submissions from all disciplines, including, but not restricted to: Adult and Organisational Learning, Computer Science, Engineering, Sociology, Industrial Psychology, Human Factors Engineering.

Manuscripts from any domain are welcomed on:

  • Reviews of state of the art of LfI;
  • Whole of systems approaches to LfI;
  • New methodologies for researching LfI;
  • New instruments for measuring LfI;
  • Inter-disciplinary insights into LfI;
  • Case studies involving new concepts to LfI;
  • Commentaries on LfI and the future for the Safety Science discipline
  • Reports on intervention studies into improving LfI
  • Approaches to facilitating and enhancing interactions between researchers, practitioners and policymakers in LfI

Timeline (updated on 5 January 2016)

 

31 March 2016 – Submission deadline for papers
1 July 2016 – Authors receive reviewers’ comments
31 September – Revised manuscripts submitted
31 December – Authors receive reviewers’ comments on revised manuscripts
1 February – Authors receive decision on manuscripts
31 March 2017 – Editorial and order of manuscripts passed onto journal administrator
End of Q3 – special issue published

 

Instructions for authors

The deadline for receipt of papers is 1st February 2016, with a projected publication date of mid 2017. All papers will be subjected to the standard peer-review procedures of the journal. Potential authors are requested to submit their paper for consideration to Professor Neville Stanton (n.stanton@soton.ac.uk), Dr Anoush Margaryan (anoush.margaryan@gcu.ac.uk), Professor Allison Littlejohn (Allison.littlejohn@open.ac.uk) prior to electronic submission so that the Guest Editors can ensure its scope and quality is suitable for the special issue.

Following approval, papers should be submitted online via the Elsevier Safety Science manuscript submission site . When specifying ‘Article Type’ authors should select ‘SI: Learning from Incidents”. Failure to do so will cause the papers to go unrecognised as belonging to the special issue.

Guidelines for authors can also be found on the Safety Science website.

Papers in updated Google Scholar citations rankings

I was pleased to find out this morning that several papers I have co-authored achieved high ranking in the recent update of Google Scholar citation metrics:

  1. The most cited paper in the journal Computers and Education (the top journal in Google Scholar Educational Technology category) in the last 5 years: Margaryan, A., Littlejohn, A., & Vojt. G. (2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers and Education, 56(2), 429-440.
  2. The second most cited paper in the Journal of Online Teaching and Learning (no 20 in Educational Technology category) in the past 5 years: Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. Journal of Online Learning and Teaching, 9(2).
  3. The fifth most cited paper in the Journal of Workplace Learning (no 17 in Google Scholar) in the past 5 years: Littlejohn, A., Milligan, C., & Margaryan, A. (2012). Collective knowledge: Supporting self-regulated learning in the workplace. Journal of Workplace Learning, 24(3), 226-238.

Thanks to Colin Milligan for pointing this out to me.