The role of learning in citizen science

Muki Haklay, Extreme Citizen Science groupDepartment of Geography, UCL

Twitter: @mhaklay / @ucl_excites

• 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

• 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)

• 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).

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

ManagersCommunity

Groups Managers IndividualsCommunity

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

√

√

√

• Collaborative science – problem definition, data collection and analysisLevel 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

• 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

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)

• 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

1. Task/game mechanics

2. Pattern recognition

3. On topic learning

5. Off topic knowledgeand skills

4. Scientific process

6. Personal development

Participationas volunteer

Source: Laure Kloetzer, University of Geneva

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

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

• Community Collaborative Rain, Hail & Snow Network

Rebecca Jacobson

Volunteer computing

Volunteer thinking

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

• 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

DIY Sensing

More information at http://publiclaboratory.org

DIY/Civic Science

• 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

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

• 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

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

Pepys estate air quality study

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

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

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

• 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

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

Participatory Sensing

• 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

Earthquake preparedness

• 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

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

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

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

• 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

• 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

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