This is a presentation from the citizen science impact event at the Open University http://www.open.ac.uk/blogs/opentel/citizen-science-impact-event-at-the-open-university/ Citizen science offer different levels of engagement to participants, which have been captured in typologies of the field (contributory, collaborative, co-created, collegial / crowdsourcing, distributed intelligence, participatory science, extreme citizen science). These typologies do no explicitly examine learning. At the same time, projects and activities striving to fulfil multiple goals (excellent scientific output, satisfying engagement, good recruitment, learning …). Within ythe range of citizen science project, we can consider different aspects of learning that are occurring in them, Projects and use examples from a range of project, and raise some aspects that can help those who are designing co-created projects.

1. The role of learning in citizen
science
Muki Haklay, Extreme Citizen Science group
Department of Geography, UCL
Twitter: @mhaklay / @ucl_excites

2. • Typologies and goals in citizen science
• Aspects of learning and examples from
contributory and collegial projects
• Learning in co-created projects at the Extreme
Citizen Science group projects
Overview

3. • Action oriented - encourage participant intervention in local
concerns, using scientific research as a tool to support civic
agendas.
• Conservation- support stewardship and natural resource
management goals, primarily in the area of ecology.
• Investigation - focused on scientific research goals requiring
data collection from the physical environment.
• Virtual - all project activities are ICT-mediated with no
physical elements whatsoever.
• Education - education and outreach primary goals, all of
which include relevant aspects of place.
Primary goals and physical environment
Wiggins & Crowston (2011). From conservation to crowdsourcing: A typology of citizen science. In System Sciences (HICSS)

4. • Contractual - communities ask professional researchers to
conduct a specific scientific investigation and report on the
results;
• Contributory - generally designed by scientists and members of
the public primarily contribute data;
• Collaborative - generally designed by scientists and members of
the public contribute data, refine project design, analyse data,
disseminate findings;
• Co-Created - designed by scientists and members of the public
working together, some of the public participants are actively
involved in most aspects of the research process; and
• Collegial - non-credentialed individuals conduct research
independently with varying degrees of expected recognition by
institutionalised science.
The 5 Cs classification
Shirk et al. (2012). Public participation in scientific research: a framework for deliberate design. Ecology and Society, 17(2).

5. After Cooper, Dickinson, Phillips & Bonney (2007) Citizen Science as tool for conservation in residential ecosystems. Ecology and Society 12(2)
Question
Study Design
Data Collection
Data Analysis and
Interpretation
Understanding
results
Management Action
Geographic scope
of project
Nature of people
taking action
Research priority
Education priority
Traditional
Science
Scientific
Consulting*
Contributory
Citizen
Science
Collaborative
Citizen
Science
Collegial
Citizen
Science /
Participatory
Action
Research
Variable Narrow NarrowBroad Broad
Managers
Community
Groups Managers Individuals
Community
Groups
Highest Medium High High Medium
Low Medium High High High
*often called Science Shops
Community Science
Co-created
Citizen
Science
Narrow
High
High
All
√
√√√
√
√
√
√
√
√
√ √
√
√
√
√
√
√
√
√
√
√ √
√
√
√Public Scientists
√
√
√

6. • Collaborative science – problem
definition, data collection and analysis
Level 4 ‘Extreme’
• Participation in problem definition
and data collection
Level 3
‘Participatory
science’
• Citizens as basic interpreters
Level 2 ‘Distributed
intelligence’
• Citizens as sensors
Level 1
‘Crowdsourcing’
Haklay (2013). Citizen Science and volunteered geographic information: Overview and typology of participation, Crowdsourcing Geographic Knowledge

7. • Analysing:
– Environment (physical/online),
– Technology (web/mobile/pen & paper),
– Engagement (levels of control over the project), and
– Relationships with professional science
• Aspects of learning and creativity are not explicit
Core typologies of citizen science

8. Citizen
Science
Awareness to
environmental
or scientific
issue
Producing
scientific
outputs
Achieving
temporal and
geographical
coverage
Achieving
inclusiveness
Increasing
scientific
literacy
Accessing
resources
Creating
enjoyable &
engaging
experiences
Balancing Citizen Science goals
• Each citizen science
project is a balancing act
between the scientific
goals, scale and depth of
engagement, benefits to
different stakeholders
(scientists, participants,
project funders)

9. • Who is learning and what are they learning?
• Is the learning aspects designed into the project?
• Which goals are addressed through the learning
process and tools?
• Is the learning evaluated and inform the project?
How?
Some questions on learning

10. 1. Task/game mechanics
2. Pattern recognition
3. On topic learning
5. Off topic knowledge
and skills
4. Scientific process
6. Personal development
Participation
as volunteer
Source: Laure Kloetzer, University of Geneva

11. A taxonomy on learning
outcomes in citizen science
projects. 3 mains categories:
1. personal development,
2. generic knowledge &
skills,
3. project-specific
knowledge and skills
Source: Laure Kloetzer, University of Geneva

12. Bioblitz etc.
Participating in Big Garden
Bridwatch (source: RSPB)
Participating in BioBlitz (source: OPAL, Esri)
Kerski. (2016) Mapping BioBlitz Field Data in ArcGIS Online Esri GIS Education Community Blog

13. • Community Collaborative
Rain, Hail & Snow Network
Rebecca Jacobson

14. Volunteer computing

15. Volunteer thinking
Hanny van Arkel. “The Dutch schoolteacher and Queen admirer who discovered Hanny’s Voorwerp”.

16. • Data collection process and protocols
• Details about the issues (e.g. bird feeding in winter)
• Organisational skills
• Familiarity with systems and procedures
(CoCoRHaS)
• New patterns or discoveries
Learning in contributory projects

17. DIY Sensing

18. More information at http://publiclaboratory.org
DIY/Civic Science

20. • New tools and social learning (sensors
development)
• Problem solving skills
• Issue specific (what is being measured and how)
• Organisational skills
• Communication and political action
Learning in collegial projects

21. Regalado. (2017) Unwrapping DIY enquiry: The study of ‘enquiry’ in DIY practice at individual, community & place levels, PhD Thesis UCL

22. • Most of the focus is on the participants – what they
learn and how
• Little research on the scientists:
– Shirk, J. (2014) Push The Edge Of Science Forward.
Expanding Considerations Of Expertise Through
Scientists' Citizen Science Work In Conservation, PhD
dissertation, Cornell University
Issues with learning

23. Extreme Citizen Science (ExCiteS) is a situated,
bottom-up practice that takes into account local
needs, practices and culture and works with broad
networks of people to design and build new devices
and knowledge creation processes that can
transform the world.
Creating technologies that are designed to be
embedded within participatory processes.
Extreme Citizen Science

24. Pepys estate air quality study

25. Diffusion Tubes
Pros Cons
Comparable to Local Authority data Not real time
Only need a step ladder and diffusion
tube
Active involvement
Easy to use Measurement in one location
Uses local knowledge
Low cost
Inclusive
Integrates with mobile apps to record
location & other details

26. Widely distributed press release
targeted at politicians and media
Follow-up with Wandsworth Council,
TfL and Mayor’s Office
Key achievement: persuading TfL to
introduce hybrid and retrofitted buses
Putney: Air Quality
Monitoring outcome

27. Ellul, Francis, and Haklay (2012), Engaging with local communities: A review of three years of community mapping. Urban and Regional Data Management, UDMS Annual
2011 - Proceedings of the Urban Data Management Society Symposium
Exploring the results

28. • Identifying the most suitable tools (diffusion tubes)
• Identifying the role of technology and mapping in
documenting the activities and sharing the results
• Using both local knowledge and scientific
knowledge
Community & researchers learning

29. Source: Mapping for ChangeEveryAware website at http://www.everyaware.eu
Participatory Sensing

32. • Sharing limitations and potential application of
monitoring
• Developing representations that express
community view and wishes for utilisation of the
information
• Developing new initiatives – progressing from
contributory, through co-design, to collegial
Community & researchers learning

33. Earthquake preparedness

35. • Different types of communities: community of
practice, interest, and place
• Adapting tools and activities to different life stages
and shared priorities – mutual learning
• Development of general training and learning
resource
Community & researchers learning

36. 64M UK population
8.5M BBC Attenborough & the Giant Dinosaur
520,000 in RSPB Big Garden Birdwatch
40,000 in British Trust of Ornithology surveys
500 in BioHacking & DIY Science
60,000 in Oxford ClimatePrediction.net
UK Engagement Escalator

37. General interest in popular science
Science blog reader + Galaxy Zoo classifier
Galaxy Zoo forum moderator
Community manager ExCiteS
Citizen science research
Galaxy Zoo / citizen science ambassador
...as well as Alice’s journey

38. Everyone
Consumption of science (passive/active)
Opportunistic or highly limited participation
Data collection and analysis
High engagement in DIY science
Joining volunteer computing or thinking
7 Levels of Engagement

39. • Learning is integral to citizen science
• It happen at all modes of citizen science, though in
different ways and in different areas
• There is a need to pay attention to the learning by
those who run and develop citizen science and not
only the participant
Summary

40. • New course: Introduction to Citizen Science and
Scientific Crowdsourcing
• Part of OPENER and DITOs projects
• MOOC + face to face course at UCL, aimed at MSc
students and practitioners
Coming in January 2018

41. Follow us:
– http://www.ucl.ac.uk/excites
– Twitter: @UCL_ExCiteS
– Blog:
http://uclexcites.wordpress.com
The work of ExCiteS is supported by EPSRC, ERC, EU
FP7, EU H2020, RGS, Esri, Forest People Program,
Forests Monitor, WRI and all the people in communities
that we’ve worked with over the years