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Teaching Data Literacy and Data Visualization as One-Credit Course - Tatiana Usova

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Teaching Data Literacy and Data
Visualization as One-Credit Course
Tatiana Usova, CMU-Qatar
LILAC 2022/Manchester, UK

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SOME CONTEXT
Before
Georgetown University in Qatar (GU-Q)
Undergraduate degree B.S. in Foreign Service
399 students
Now
Ca...

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A PICTURE IS WORTH A THOUSAND WORDS
3
WHY?
Information is the power.
Data is the new oil in the digital economy . – WIRED,...

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Teaching Data Literacy and Data Visualization as One-Credit Course - Tatiana Usova

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Presented at LILAC 2022

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Teaching Data Literacy and Data Visualization as One-Credit Course - Tatiana Usova

  1. 1. Teaching Data Literacy and Data Visualization as One-Credit Course Tatiana Usova, CMU-Qatar LILAC 2022/Manchester, UK
  2. 2. SOME CONTEXT Before Georgetown University in Qatar (GU-Q) Undergraduate degree B.S. in Foreign Service 399 students Now Carnegie Mellon University in Qatar (CMU-Q) Undergraduate degree B.S. in Biological Sciences, Business Administration, Computer Science and Information Systems 439 students Education City, Doha, Qatar 12 square kilometer campus housing branches of six American universities where students receive Western education in a Middle Eastern setting. 2 Image source: https://mosqpedia.org/en/mosque/154
  3. 3. A PICTURE IS WORTH A THOUSAND WORDS 3 WHY? Information is the power. Data is the new oil in the digital economy . – WIRED, 2014 https://www.thejobnetwork.com/10-in-demand-skills-need-get-hired-fast/ Example The ability to discover, evaluate, and use information and data is a core competency of CMU students
  4. 4. ACTIVITY Pair with your neighbour and discuss what the term “data literacy” stands for. Post your answer to menti.com (Code: 1048 1614) 4 menti.com
  5. 5. “ 5 “a suite of data acquisition-, evaluation-, handling-, analysis- and interpretation-related competencies” (Prado and Marzal, 2013, p. 124). “specific skill set and knowledge base that enables us to transform data into information and ultimately into actionable knowledge” (Koltay, 2017, p.9) DATA LITERACY
  6. 6. PROBLEM: Data Visualization Literacy is Low 6 (Börner et al., 2016)
  7. 7. “ 7 DATA VISUALIZATION LITERACY NAS Colloquia, 2018, 7’51”
  8. 8. PROJECT ROADMAP 8 1 3 5 6 4 2 Conversations with alumni and environmental scanning, spring 2019 Curriculum committee approval, October 2019 Online teaching: one- credit course (Tableau), Jan-Feb 2021 Course design, summer 2019 Pilot : in person one-credit course (Venngage), Jan –Feb 2020 Hybrid: two one-credit courses (Tableau), Jan-Feb 2022
  9. 9. Course structure and learning objectives 9
  10. 10. COURSE LEARNING OUTCOMES We prepare students to be responsible readers, interpreters and communicators of data. At the end of the course they ◉ Understand effective design principles and be able to apply them in visual communication. ◉ Grasp the concept of a dataset and know where to find and gather datasets as well as how to interpret them and generate informative visualizations. ◉ Use digital tools to organize and display quantitative and qualitative data. ◉ Incorporate effective visual design and data visualization into personal and professional self-promotion. 10
  11. 11. PILOT COURSE SETUP 11 1-credit course 12,5 contact hours (2hrs/week, in person) 14 students in total active and collaborative learning
  12. 12. OUTLINE OF THE COURSE 12 Lesson Objectives Activities 1. Data Visualization and Basic Design Principles Understand core concepts of effective design. Develop the ability to interpret and critically analyse charts. Generate charts in Google Sheets that accurately convey the meaning of data. In class: evaluate and critique assigned graphs from the news media, spot misrepresentations of information. Homework: create the best-suited graphs in Google Sheets using provided data; complete online quiz. 2. Data discovery and processing: how to find, treat and cite data Locate and utilize credible datasets. Evaluate the quality of data. Treat, clean and organize data. Create multivariable charts. Properly attribute data sources. In class: clean a given dataset and prepare a multivariable visualization. Homework: explore a list of data portals; pick one dataset, clean and organize it; create easy to understand graphs.
  13. 13. 13
  14. 14. 14 Which of the following bar charts has the best design? ACTIVITY
  15. 15. 15 IN CLASS ASSIGNMENT – DATA CLEANING
  16. 16. OUTLINE OF THE COURSE (continued) 16 Lesson Objectives Activities 3. Charts, Graphs, and Maps. Using Venngage Software Understand the mechanics of the Venngage software. Generate various types of visualizations (graphs, timelines and maps) using the Venngage tool. In class: use Venngage to create graphs and geo visualizations from provided datasets. Homework: use Venngage and create a graph and a geo-visualization from a real-world dataset. 4. Storytelling with Data Sharpen skills of information presentation in Venngage. Create infographics. Become familiar with Venngage templates. In class: create infographics in Venngage using provided data. Homework: choose a dataset and create an infographic in Venngage; conceptualize the final project, conduct exploratory data research and analysis, write a short synopsis of the idea.
  17. 17. 17 INFOGRAPHIC a fun and quick way to learn about the topic without a ton of heavy reading. Websites with datasets used: • UNData • The World Bank - Data Catalog • Qatar Ministry of Development Planning and Statistics
  18. 18. OUTLINE OF THE COURSE (continued) 18 Lesson Objectives Activities 5. Tools for Independent Learning and Skill Building Understand the importance of the LinkedIn profile and identify ways to enhance an online web portfolio. Build a data visualization dashboard in Venngage. In class: present the “elevator pitch” project proposal, seek feedback and ideas for improvement. Homework: prepare the final project—visual presentations of data applying data visualization best practices. 6. Final projects presentation. Wrap-up and course assessment Present a storyboard with visualizations of insights gained from data analysis. Effectively communicate the connection between visualizations following the narrative thread. In class: present the project; participate in a course wrap-up discussion; complete the course survey.
  19. 19. STUDENTS’ FINAL PROJECTS 19
  20. 20. ASSESSMENT AND CONCLUSIONS Data Literacy imperative Leadership role of librarians Upskilling
  21. 21. FUTURE DIRECTIONS continue as a one-credit course with improved instruction offer two one-credit bearing courses: one at a basic level and one more advanced teach a three-credit course with enhanced content, and more sophisticated technology tools (e.g. Tableau) 21 collaborate with faculty on course-integrated instruction, add data visualization to course content
  22. 22. REFERENCES 22 Börner, K., Maltese, A., Balliet, R. N., & Heimlich, J. (2016). Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors. Information Visualization, 15(3), 198–213. https://doi.org/10.1177/1473871615594652 Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), 3–14. https://doi.org/10.1177/0961000615616450 NAS Colloquia. (2018, March 28). Data visualization literacy—Katy Borner. [Video]. Youtube. https://www.youtube.com/watch?v=N5wYoIR1K_4 Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri, 63(2), 123–134. http://dx.doi.org/10.1515/libri-2013-0010 Usova, T., & Laws, R. (2021). Teaching a one-credit course on data literacy and data visualisation. Journal of Information Literacy, 15(1), 84-95. https://doi.org/10.11645/15.1.2840
  23. 23. THANKS! Any questions? 23
  24. 24. Tatiana Usova Director of the Library, Carnegie Mellon University in Qatar Email: tusova@qatar.cmu.edu Telephone: 974 4454 8403 Presentation template by SlidesCarnival

Editor's Notes

  • Data Visualization Literacy - Katy Borner. 7’51”
    https://www.youtube.com/watch?v=N5wYoIR1K_4

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