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Grant Drawve, Casey Harris, Patricia Herzog gave a presentation at the Teaching and Faculty Support Center (TFSC) Winter Symposium titled “Data Literacy in Practice: Working and Understanding Real Data.” Across a wide range of employment fields, emphasis is being placed on the essentials of database construction and management, analysis, and data transparency, what administrators and […]

Grant Drawve, Casey Harris, Patricia Herzog gave a presentation at the Teaching and Faculty Support Center (TFSC) Winter Symposium titled “Data Literacy in Practice: Working and Understanding Real Data.”

Across a wide range of employment fields, emphasis is being placed on the essentials of database construction and management, analysis, and data transparency, what administrators and instructors refer to as “data literacy.” Our goal has been to develop innovative data modules – short, 10-20 minute videos explaining core data literacy concepts with accompanying exercises – designed to meet this need, while simultaneously improving and updating pedagogy techniques to better highlight data literacy skills for our students.

We see four specific advances with our data modules.

  1. The implementation of these teaching tools increases student familiarity and capacities with real world, marketable datasets, like those generated by big data and employed across a range of occupations. Many faculty already utilize large, multi-faceted secondary datasets for their own research and bring their findings to both undergraduate and graduate classes to build some of these skills. Rather than relying on disconnected data skill development across faculty with different databases, our modules provide a cohesive series of tools that “stack” easily on one another for consistency.
  2. These modules work well in both traditional face-to-face and online courses, thereby enhancing our Global Campus curriculum alongside more traditional learning environments.
  3. The modules are relevant for multiple student levels (e.g. freshman through graduate). By creating a series of consistent data literacy modules throughout our course offerings, we avoid front- or back-loading the acquisition of essential data analytic skills so students gain consistent exposure as they traverse through classes.
  4. Faculty benefit from having collaborative discussions through engagement with their own research and teaching as they develop these modules together. By incorporating these modules in many of our course offerings, each module will be strengthened by drawing upon the collective strengths of the faculty, including theoretical, empirical, and substantive resources across our different disciplines and subfields.

There will be 20 teaching modules developed and disseminated through the Center for Social Research (CSR). Each teaching module will include a brief review of topic importance, module instructions, and a tailored dataset. Most will conclude with students applying the skills learned in the module to a brief exercise using the same database illustrated in the video. Student and faculty feedback will be provided to module developers for revisions prior to finalization. The modules would provide hands-on exercises for students to explore.

Examples of the Modules

  1. What kind of data? Qualitative or Quantitative
  2. What is the unit of analysis? Individuals or collectives
  3. Selecting a theoretical framework? Inductive or deductive
  4. What do other studies do? Different approaches or extending a new area
  5. How to operationalize a concept? Existing data or new measures
  6. Data Management 1 – Merging and filtering
  7. Data Management 2 – Changing units of analysis
  8. Comparing data sources – Triangulation and understanding sources
  9. Frequencies and distributions
  10. Central tendency – Mean, Median, and Mode
  11. Variability – Range, standard dev, skew, and kurtosis
  12. Data Visualization 1 – Bars, lines, pies, and pivot tables
  13. Data Visualization 2 – Special cases of data visualizations
  14. Inferential Statistics 1 – Comparing means across groups (T-test; ANOVA)
  15. Inferential Statistics 2 – Dealing with categorical data (Chi-square)
  16. Inferential Statistics 3 – Continuous data (Correlation; Regression)
  17. Coding – Deductive, descriptive, systematic, and overlapping
  18. Categories – Child and parent nodes
  19. Queries – Inquiries of the data to check for patterns
  20. Presenting – Displaying data in PowerPoint

 

Grant Drawve faculty picture

Grant Drawve is an assistant professor of Sociology in the J. William Fulbright College of Arts & Sciences

Casey Harris faculty picture

Casey Harris is associate professor of Sociology and Co-Director for the Center for Social Research in the J. William Fulbright College of Arts & Sciences

Patrica Herzog

Patricia Herzog is associate professor of Sociology and Co-Director for Center for Social Research in the J. William Fulbright College of Arts & Sciences.

This content was developed from a presentation by Grant Drawve, Casey Harris, Patricia Herzog which was sponsored by The Wally Cordes Teaching and Faculty Support Center (TFSC) at the University of Arkansas.

The presentation can be downloaded and viewed as a PDF: coming soon