Me and My Big Data Project
Download as PPTX, PDF1 like70 views
The document discusses the 'Me and My Big Data' project focused on developing citizens' data literacy, detailing its structure, methodologies, and social context relating to digital inequality. It emphasizes the need for improved digital and data literacy frameworks, addresses various terminologies related to digital inequality, and highlights the importance of understanding data management and social implications. The project includes literature reviews, national surveys, workshops, and aims to produce educational materials and reports to enhance citizens' engagement with digital technologies.
Me and My Big Data Project
- 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
- 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.
- 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