Me and My Big Data – Developing Citizens' Data Literacies // @LivUni @NuffieldFound project // website:bit.ly/meandmybigdata

1. Me and My Big Data –
Developing citizens’ data literacy
Prof: Simeon Yates
Dr. Elinor Carmi; Ms. Alicja Pawluczuk; Dr. Tamara West:
University of Liverpool
Prof. Bridgette Wessels: University of Glasgow
Dr. Eleanor Lockley: Sheffield Hallam University
Twitter: @meandmybigdata
Website: tinyurl.com/yb5fay5z

2. Structure of talk
• Nuffield Project
• Project plan
• Social and policy and
context
• Defining Digital and Data Literacies
• Definitions
• Policy
• Literature
• Existing data
• Types of users
• Digital Media Literacies
• Perceptions and practices
• Future steps
• Conclusions

3. Project plan
• Literature review
• Secondary data analysis
• National survey
• Citizen workshops
• Educational materials
• Policy workshops
• Salon Events
• National Salon
• Project conference
• Project special issue
• Final Report

4. Social context

5. Digital inequality - terminologies
• Digital divide
• inequity in access to contemporary
digital media
• ICT divide
• inequity in access to information and
communication technologies
• Information divide
• primarily inequity in access to
information sources
• Digital inequality
• inequities in the uses, outcomes and
value of digital media and technology
engagements
• Digital literacy
• With the implication that there may
be variations and inequities in levels
of digital literacy
• Digital inclusion
• Processes or policies to address
digital inequalities, especially around
access and use
• Digital engagement
• Broader questions of how motivate
and support users and citizens to
engage with digital technologies

6. Measures of digital inequality
• Binary measures of access to digital technology or not
• Such as PC or device ownership or internet connection
• Different levels of access
• Such variations in broadband speed or shared, rather than individual,
access to devices in the home
• Differences in digital skills/literacies
• Such as ability to use basic features vs complex system use or deep skills
in specific areas (media use, gaming, coding)
• Differences in levels of use
• Such as measures of frequency of use or complexity of use
• Differences in types of use
• Either variety of use (extensive broad use vs narrow use) or specific key
types of use (e.g. educational use)
• Differences in benefits from use
• Personal, financial, social, cultural, health etc.
• Differences in hazards from use
• Levels of potential risks and harms from using the technology

7. What are digital skills
• Basic Digital Skills Framework Doteveryone (2015)
• "This set out five main areas of digital capability and what the categories
mean for both individuals and organizations, and in terms of online safety.
The five areas constituting basic digital skills are:
• 1) Managing information: Find, manage and store digital information and content.
• 2) Communicating: Communicate, interact, collaborate, share and connect with
others.
• 3) Transacting: Purchase and sell goods and services; organise your finances; register
for and use digital government services.
• 4) Problem Solving: Increase independence and confidence by solving problems and
finding solutions using digital tools.
• 5) Creating: Create basic digital content in order to engage with digital communities
and organisations.

8. DotEveryone Digital
Understanding Model 2018

9. Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

10. Data Management
Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

11. Thinking with data
Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

12. Personal data
Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

13. Data participation
Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

14. Data Management
Thinking with data
Personal data
Data participation
Data literacy – definitions in use
• Definitions are very diverse
and often multi-faceted
• Range from a focus on
analytics to general critical
awareness
• We have identified 13 key
components
• The lease defined/discussed
is the use of data by citizens
• Overlaps considerably with
the higher end of digital and
internet skills
• Accessing
• Assessing (e.g. credibility)
• Interpretation
• Ethical use
• Data Management
• Manipulation/Visualization
• Problem solving using data
• Communicating with data
• Critical analysis of data (e.g. cultural
contexts, data)
• Understanding of data collection and
processing
• Data safety (e.g. skills to manage and
control ‘digital traces’
• Understanding Data Society (impact,
procedures)
• Data hacking

15. UK Non and Limited Users – 11M People
(2014 and 2016)
A Chi-square indicates a significant association
between NRS Social Class and internet user types, (c2
(21, n=1890) = 286.689, p < 0.000, medium to large
effect size Cramer’s V = 0.225)

16. Cultural capital - Education
A Chi-square indicates a significant
association between type of internet
user and age on leaving education,
(c2 (35, n=1890) = 445.106, p <
0.000, medium to large effect size
Cramer’s V = 0.217)
All Limited user types are likely to have left
education at 16 – also note social media limited
users as a key group for later discussion

17. It is not just a function of age – young limited users

18. ICO report 2019
After finding out information about how the adtech process works, levels of
perceived acceptability decreased.
19
Base: 1690 (UK 18+ year olds who visit free to use websites)
MQ4/18. How acceptable or unacceptable is it that some
websites display adverts in return for the websites being
free to use?
Before given
information on
adtech
After given
information on
adtech
63%
acceptable
14%
unacceptable
36%
acceptable
43%
unacceptable
Those who disagree that they’d prefer to
see adverts on websites that are relevant
to them
61%
Those who feel that they have no control
over which adverts are shown to them on
these websites
59%

19. How is Google Funded
Young limited internet users

20. Data and internet concerns and protections
Data&
digital
Dangerous/
Harmful/
Hateful
Unregulated
Illegal
Harmfulkids
Violent
Social classes A&B more likely
to have concerns but also
more likely to act to protect
themselves online

21. Sharing data – everyone is
confident…but nobody checks
Personal data
Protection in general
Photos of me Photos of others
Posts by me Location data

22. Checking facts – most people don’t
•77%
•Don’t check sources
•Higher education
makes you slightly
more likely to check

23. Next steps
• Literature review
• Nearly complete
• Secondary data analysis
• Ongoing
• National survey
• To go into field in August 2019
• Citizen workshops
• Late 2019
• 2020
• Educational materials
• Policy workshops
• Project conference
• Project special issue
• Final Report
• End 2020

24. Questions?
Follow us on Twitter: @meandmybigdata
Website: tinyurl.com/yb5fay5z
Email: simeon.yates@Liverpool.ac.uk
elinor.carmi@Liverpool.ac.uk