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
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|
· 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
· SRL microanalysis
· Experience sampling methods (eg diary or tracking devices)
· 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
· 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
· 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.
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.
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.
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.
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
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:
- 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.
- Surround yourself with the smartest people you can to challenge you and push you to the next level.
- 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.
- 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’.
- 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.
Updated on 5 January 2016:
The timeline for our Safety Science special issue on Learning from Incidents has been extended as follows:
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
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)
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 (firstname.lastname@example.org), Dr Anoush Margaryan (email@example.com), Professor Allison Littlejohn (Allison.firstname.lastname@example.org) 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.
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:
- 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.
- 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).
- 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.
Pleased to find out that my abstract ‘Reconceptualising professional learning within emergent digitally-mediated work practices’ has been accepted for the forthcoming symposium of the Dynamics of Virtual Work research network.
Abstract: In many domains work has become increasingly complex, reliant on expertise distributed across a range of specialisms and involving novel problems at the boundaries of human knowledge (Boisot et al, 2011). In parallel, the unfolding digital transformation of work is catalysing new formations and constellations in the workplace that challenge traditional patterns of individual agency, organisation, power, responsibility and learning (Littlejohn and Margaryan, 2013). New forms of organisation mediated by digital technology include crowdwork, networked science, nomadic work and other types of distributed work (Bietz, 2013; Nickerson, 2013; Nielsen, 2012). These developments are having a profound effect on society and work, but are yet to have a significant effect on how professional learning is conceptualised and organised. Contemporary work practices require new forms of professional learning that align with the new spatial and temporal reconfigurations of workplaces, new work cultures, new networks of knowledge, and new requirements pertaining to the development and use of digital technologies. Conventional forms of professional learning such as formal training enable large numbers of people to reach a specific level of competency; however these forms of learning are unlikely to meet the learning needs of people in these new work contexts. Established forms of professional learning have largely not taken advantages of the opportunities around how people collaborate to learn, emergent knowledge networks, multiple ways in which people and knowledge resources can be brought together to enhance learning, and how digital technologies can extend access to these learning opportunities and resources. A fundamental rethink of how professional learning aligns with current trends in work, technology and society is required. In this presentation, I will discuss key implications of digital reinstrumentation and the emergent work practices for professional learning. Drawing on four concepts from learning sciences, sociology of work and technology-enhanced learning – self-regulation (Zimmerman, 2006), objectual practice (Knorr-Cetina, 2001), networked learning (Milligan, Littlejohn and Margaryan, 2014) and charting (Littlejohn, Milligan, and Margaryan, 2012) – I will outline some ways in which learning within emergent digitally-mediated work practices may be reconceptualised and fostered.
Bietz, M. (2013). Distributed work: Working and learning at a distance. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 28-38). London: Routledge.
Boisot, M., Norberg, M., Yami, S., & Nicquevert, B. (2011) (Eds.) Collisions and collaboration: The organisation of learning in the Atlas Experiment at the LHC. Oxford: Oxford University Press.
Knorr-Cetina, K. (2001). Objectual practice. In Schatzki, T., Knorr-Cetina, K., & Savigny, E. (Eds), The practice turn in contemporary theory (pp. 175-188). London: Routledge.
Littlejohn, A., & Margaryan, A. (2013) (Eds.). Technology-enhanced professional learning: Processes, practices and tools. London: Routledge.
Littlejohn, A., Milligan, C., & Margaryan, A. (2012). Collective knowledge: Supporting self-regulated learning in the workplace. Journal of Workplace Learning, 24(3), 226-238.
Milligan, C., Margaryan, A., & Littlejohn, A. (2014). Workplace learning in informal networks. Journal of Interactive Media Environments, special issue ‘Reusing Resources – Open for Learning. [Online] file:///Users/ama11/Downloads/325-2585-1-PB%20(2).pdf
Nickerson, J. (2013). Crowd work and collective learning. In Littlejohn, A., & Margaryan, A. (Eds.). Technology-enhanced professional learning: Processes, practices and tools (pp. 39-49). London: Routledge.
Nielsen, M. (2012). Reinventing discovery: The new era of networked science. Princeton: Princeton University Press.
Zimmerman, B. (2006). Development and adaptation of expertise: The role of self-regulatory processes and beliefs. In Ericsson, A., Charness, N., Feltovich, P., & Hoffman, R. (Eds.), The Cambridge handbook of expertise and expert performance (pp. 705-722). New York: Cambridge University Press.