Author: Anoush Margaryan

Call for Expressions of Interest – Marie Sklodowska-Curie Individual Fellowships

Call for expressions of interest for Marie Curie Individual Fellowship in “Implementation of Artificial Intelligence and Digitisation in workplaces in relation to the redesign of jobs and the creation of new job”

University College London, Institute of EducationDepartment of Education, Practice and Society

Location: London

Salary: basic salary £32,548.00 gross per annum (Grade 7, Spinal point 30), plus London allowance £3,031.00 per annum and top up allowance ranging from £8,750.00-£20,000.00 depending on pension contributions and dependants.

Hours: Full time

Contract type: Fixed term

Closing date for Expressions of Interest: 6 August 2018

Closing date for submission of applications: 12 September 2018

 

 The Fellowship

The Marie Sklodowka Curie Individual Fellowships (MCIF) are prestigious fellowships funded by the European Commission. They offer a generous allowance for 1-3 years including mobility, family and research allowance. Applicants can be from any country, must hold a PhD (or equivalent), and must not have resided or carried out their main activity in the UK for more than 12 months in the 3 years immediately prior to the MCIF deadline on 12 September 2018. The MSCA have a bottom-up approach, i.e. research fields and topics are chosen freely by the applicants.

 

Introduction

The Department of Education, Practice and Society (EPS) at the University College London (UCL) Institute of Education (IoE) welcomes expressions of interest from postdoctoral researchers with an excellent track record of research and publication to apply jointly with a supervisor from EPS/IoE to the European Commission Marie Sklodowska-Curie Individual Fellowship Scheme. Selected candidates will receive dedicated support from two or more IoE Professors as well as from EPS peer-review system to develop their proposal and application for submission to the European Commission by 12 September 2018.

The UCL/IoE lead professor is:

  • Professor David Guile, Professor of Education and Work and Head of Department of Education, Practice and Society

and, the co-supervisor is:

 

Research themes

Expressions of Interest that take the following issues as their starting point – the implementation of Artificial Intelligence and Digitisation in workplaces in relation to the redesign of jobs and the creation of new jobs – and are aligned with one of more of the following research themes are welcome:

  1. The impact of the implementation of Artificial Intelligence and Digitisation in workplaces on professional formation, including learning
  2. Practices and processes of workplace learning and skill development in emergent forms of digital work such as online labour platforms or gig work
  3. Policy and practice implications of digitisation and AI for vocational educational, higher education and learning throughout working life
  4. Skill development and skill matching in platform workplaces
  5. Practices and processes of worker self-organisation and networking for learning and skill development in platform labour

Candidates with a (interdisciplinary) background in, including but not limited to, Education, Sociology, Learning Sciences, Psychology, Communication Studies, Information Science, Business and Organisational Studies/Human Resource Development are welcome to apply.

 

How to express your interest

Expressions of interest should include:

  1. A copy of your CV (including a full list of your publications and research projects)
  2. A 2-3 page Research Proposal Summary, which includes details as to which research area(s) from the list above you are interested in
  3. A 1-2 page motivation letter identifying synergies with key research areas listed above

Please email your EoI pack as one single PDF file attachment, putting ‘MCIF Expression of Interest’ as your email subject to IOE.recruitment@ucl.ac.uk  by Monday 6 August 2018.  

Expressions of interest will be selected on the basis of the quality of the research idea and the candidate’s track record. Candidates will be invited to make a formal application with their EPS/IoE supervisors. Results will be announced on Monday 13thAugust.

Please note: UCL Research Finance require the successful proposal, with budget, to be submitted to them 5 days before the EU’s MCIF deadline 12thSeptember. This means the final version of the successful proposal will have to be submitted to Professor Guile and Professor Margaryan on Monday 3rdSeptember.

For further information please contact Professor David Guile ( d.guile@ucl.ac.uk ) or Professor Anoush Margaryan ( anoush.margaryan@gmail.com ).

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Crowdworkers’ motives to take up crowdwork

One of the issues I explored as part of the survey of crowdworkers was their motives to engage in this form of work.

Specifically, the participants were presented with a list of 16 motives synthesised from crowdwork literature and asked to select all motives that applied in their case.  Participants were also given an opportunity to indicate any other motives not included in the list.

Findings (Figure 1) suggest that the key motives are to earn supplementary or main income, with crowdwork tasks considered fun and a fruitful way of spending one’s time while earning money. Also, being their own boss and having control over their own schedule were indicated relatively frequently as reasons to take up crowdwork. Few crowdworkers appear to engage in this form of work due to inability to find or perform traditional work (10% and 4% respectively).

Motives

Figure 1. Crowdworkers’ motives to engage in crowdwork (n=182)

Comparing the responses of microworkers and online freelancers (Fig. 2), more online freelancers report crowdwork as their primary source of income corraborating findings from previous studies of crowdwork.

Also, we observe that considerably fewer online freelancers than microworkers report crowdwork tasks being fun as a key motive, but considerably more online freelancers cite being their own boss, having control over their schedule, following their passion, having more choice of the projects they can do, and earning more through crowdwork than they could through traditional work as key reasons to take up crowdwork.

MotivesPerGroup

Figure 2. Online freelancers’, OF (n=15) and microworkers’, MW (n=167) motives to engage in crowdwork 

Other motives indicated by the crowdworkers included:

  • “As more people use [crowdwork platform] to find people for project work, the network effect almost requires that I use it; it is an important source of work referrals.”
  • Learning skills and gaining work experience”.
  • “I just finished my studies and am currently searching for a traditional (part-time) employment in addition to crowdwork, mostly for insurance reasons.”
  • “In [my country] the incomes from traditional work are terrible“.
  • “It’s best online earning platform I’ve found so far.”

 

Do crowdworkers self-identify as entrepreneurs?

As part of the survey of crowdworkers’ learning practices, I asked the participants about their self-identity as entrepreneurs.

In particular, I asked the crowdworkers if they considered themselves to be entrepreneurs in line with the following definition: “‘Entrepreneur’ means a person who organises and manages their own business exercising considerable personal initiative and taking on financial risk. Entrepreneurs include people who are self-employed, those who are a sole owner, partner or a majority shareholder of a small, medium, or large company”.

The majority of crowdworkers (52%) reported not identifying as entrepreneurs (Figure 1):

Entrepreneurship.png

Figure 1. Crowdworkers’ reported self-identity as entrepreneurs (n=182)

More online freelancers than microworkers reported self-identifying as entrepreneurs (Figure 2).  Those who selected ‘other’ stated that they weren’t entrepreneurs yet, but were planning to start a business in the future.

EntreprenuersByGroupFigure 2. Reported entrepreneurial self-identity among microworkers, MW (n=167) and online freelancers, OF (n=15) 

 

Crowdworkers’ use of self-regulated learning strategies

My previous blogposts in the series on crowdworkers’ learning practices are available here, here, here, and here.

One aspect of the surveys of crowdworkers learning practices I’ve been recently conducting has been focused on scoping the range and frequency of use of of self-regulated learning (SRL) strategies undertaken by crowdworkers.

The questionnaire included a sub-scale of 34 items grounded in Zimmerman’s 3-phase model of self-regulated learning scoping crowdworkers’ strategies of planning, implementing and reflecting on their workplace learning.

The initial results from the sample of 167 microworkers and 15 online freelancers are as follows:

  • The possible range of SRL scores based on the questionnaire: 0 – 102
  • The actual SRL score ranges in this sample are:
    • microworkers: 4-99
    • online freelancers: 10-76
  • The sub-groups by SRL score are:
    • low SRL: 0-34
    • medium SRL: 35-70
    • high SRL: 71-102

1. What is the distribution of high, low and medium SRL scores among this sample?

SRLScoresFigure 1. Distribution of SRL scores among the sample of crowdworkers, percentages (n=182, including microworkers (MW) n=167 and online freelancers (OF) n=15).  

The bell curve distribution of SRL scores is in line with our previous surveys among ‘conventional’ knowledge workers.

 

2. What are the most prevalent SRL strategies among crowdworkers across the three phases of Zimmerman’s model?

By ‘most prevalent’ I mean SRL strategies that crowdworkers reported using ‘most of the time’ and ‘always’ (those who reported using a strategy only ‘sometimes’ are excluded from this analysis).

The initial results are shown in Figures 2-4 below.

Overall, we observe that crowdworkers use a wide range of self-regulated learning strategies across all phases, setting and modifying their own learning goals, strategies and performance standards and reflecting on their learning from crowdwork tasks.

Self-efficacy and intrinsic motivation are prevalent among crowdworkers (determined by responses to statements such as ‘important to learn new things in crowdwork tasks’, ‘confident can handle most demands in crowdwork’, prefers tasks that require to learn something new’, ‘meets learning goals’).

Despite the nature of crowdwork tasks that are designed to be completed individually and autonomously, some crowdworkers apply social learning strategies. Examples of social learning strategies are reaching out to others (36% of microworkers and 33% of online freelancers reported doing this most of the time or always); considering how what they have learned from crowdwork may be of interest to their peers (27% of microworkers and 39% of online freelancers do this most of the time or always); or sharing their learning from crowdwork with others (30% of online freelancers and 13% of microworkers).

The patterns of use of SRL strategies are broadly similar across both groups, but there are some differences, most notably:

  • More microworkers report regularly allocating time to work on their learning goals (22% of microworkers vs 7% of online freelancers)
  • More microworkers report regularly reflecting on their performance on crowdwork tasks (64% of microworkers vs 13% of online freelancers)
  • More microworkers report regularly sharing their reflections on their learning with others (20% of microworkers vs 0% of online freelancers)

Further analysis of the data will help determine if these differences are statistically significant and develop possible explanations for the differences.

SRLPlanning

Figure 2. SRL Planning strategies among crowdworkers 

 

SRLImplementation

Figure 3. SRL implementation strategies among crowdworkers

 

SRLReflection

Figure 4. SRL reflection strategies among crowdworkers

 

 

 

 

 

Nature of work tasks in crowdwork: Microwork vs online freelancing

As part of the survey of crowdworkers’ learning practices, I explored the nature of tasks they undertake within crowdwork platforms using two sets of previously validated and published scales: the Classification Structure of Knowledge-intensive Processes and some items from the ‘Knowledge Characteristics’ sub-scale of the Work Design Questionnaire.

In particular, the participants were asked to indicate which of the following 15 task types most closely described their typical crowdwork tasks (they could choose all options that applied):

  1. My crowdwork tasks are mostly routine
  2. My crowdwork tasks are highly reliant on formal processes
  3. My crowdwork tasks don’t give me freedom to decide what should be done in any particular situation
  4. My crowdwork tasks are mostly systematically repeatable
  5. My crowdwork tasks are highly reliant on formal standards
  6. My crowdwork tasks are dependent on integration across functional or disciplinary boundaries
  7. My crowdwork tasks are improvisational/creative
  8. My crowdwork tasks are highly reliant on my deep expertise/personal judgement
  9. My crowdwork tasks are dependent on collaborating with others
  10. My crowdwork tasks are highly reliant on my own individual expertise/experience
  11. My crowdwork tasks involve solving problems that have no obvious correct answer
  12. My crowdwork tasks involve dealing with problems I have not met before
  13. My crowdwork tasks require unique ideas/solutions to problems
  14. My crowdwork tasks require me to use a variety of skills to complete the work
  15. My crowdwork tasks require me to use a number of complex or high-level skills

Here are the results from the two groups: microworkers (Figure 1) and online freelancers (Figure 2). Among microworkers the three most prevalent characterisations of tasks were routine, systematically repeatable and requiring a variety of skills. And among online freelancers the most prevalent tasks were those that required a variety of skills and uniquie ideas and solutions, dealing with novel problem, and were reliant on their own individual expertise and experience. 

NatureOfTasksMW

Figure 1. Microworkers’ perceptions of the nature of their crowdwork tasks 

NatureOfTasksOF

Figure 2. Online freelancers’ perceptions of the nature of their crowdwork tasks

I’m working on a paper comparing these findings with earlier findings from a study of how conventional workers describe the nature of their tasks to see if there are any statistically significant differences and whether a similar typology emerges from the crowdwork settings. Stay tuned!

 

 

Workplace learning activities in crowdwork: 2016 vs 2017 survey

Yesterday, I wrote about the initial findings of a survey scoping the range and frequency of use of workplace learning activities among crowdworkers.   

This is the second iteration of the survey, the first survey was carried out in March 2016 within the same two platforms, but involving different participants. In the 2016 sample a larger proportion of crowdworkers reported using social learning activities, as well as online courses and tutorials. 

I plan to compare the demographic details and other key characteristics of the participants of both survey iterations to identify potential explanations for these differences.

How do crowdworkers learn? Workplace learning activities

This is the first blogpost in the series about learning practices of crowdworkers.

One of the foci of the study is how crowdworkers learn on-the-job: what  types of workplace learning activities they undertake and what learning strategies they use to self-regulate their learning.   The range and frequency of use of learning activities and learning strategies that people undertake in the workplace give us an indication of learning-intensity of a job (that is, the extent to which people need to regularly acquire new skills and knowledge to be able to maintain their job).   Crowdwork is often presumed to be low learning-intensity, low-skill, lacking in professional development opportunities and preventing workers for applying and developing their skills and know-how.  So it’s useful to scope the range and frequency of use of workplace learning activities and strategies among crowdworkers to see what, if any, empirical base there is to these assumptions about crowdwork.

This blogpost is focused on workplace learning activities. The resuts reported here are based on 182 survey responses (167 microworkers and 15 online freelancers). I’m currently collecting more survey responses from online freelancers in order to balance out the sample.

In the survey, I asked the participants to indicate how frequently they used the following 14 workplace learning activities within the last 3 months as part of their work on the crowdwork platforms (never, rarely, frequently or very frequently):

  1. Acquiring new information to complete their crowdwork tasks
  2. Working alone to complete their crowdwork tasks
  3. Collaborating with others to complete their crowdwork tasks
  4. Following new developments in their field
  5. Performing tasks that are new to them
  6. Asking others for advice
  7. Attending a training course/workshop to acquire knowledge/skills for their crowdwork
  8. Taking free online courses or webinars (e.g. Coursera) to acquire knowledge/skills for crowdwork
  9. Using paid online tutorials (e.g. Lynda) to acquire knowledge/skills for crowdwork
  10. Reading articles/books to acquire knowledge/skills for crowdwork
  11. Observing/replicating other people’s strategies
  12. Finding a better way to do a task by trial-and-error
  13. Thinking deeply about their work (e.g. what they could do better next time)
  14. Receiving feedback on their crowdwork tasks (e.g. from a client or peers)

Below are the survey results showing the percentage of the crowdworkers who reported using each learning activity ‘frequently’ or ‘very frequently’.

WLAOverallFigure 1. Percentage of crowdworkers who reported using these workplace learning activities frequently or very frequently

From this chart, crowdworkers most frequently learn by working alone on novel tasks, acquiring new information, following new developments in their fields, seeking better ways to do the tasks by trial-and-error and reflecting deeply on their work.

Crowdworkers reported some other learning activities not included in the list above, such as:

  • Watching YouTube videos
  • Participating in project groups on specific tasks
  • Participating in platform-specific online fora
  • Discussing ideas with others
  • Reading platform-specific blogs
  • Watching news in foreign languages to improve language skills
  • Taking private lessons to improve skills in a particular area
  • Scoping and learning highly-demanded technologies trending on specific crowdwork platforms

It is  interesting that just over a third of the respondents reported observing and replicating other people’s strategies as a key way in which they learn.  Also, some undertake social learning activities such as asking others for advice, collaborating with others, receiving feedback. How does this sort of mimetic and cooperative learning take place in a distributed, digital online workplace? What are the underpinning mechanisms and processes and what is the nature of the connections? These questions will be further explored in the interviews with crowdworkers.