Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR)
•Download as PPT, PDF•
3 likes•1,385 views
Presentation given at ACRL 2015, with Christine Murray, on teaching undergraduate students to discover and evaluate datasets for secondary data analysis.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (6)
Similar to Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR)
Similar to Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR) (20)
Recently uploaded
Recently uploaded (20)
Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR)
- 1. Promoting Data Literacy at the Grassroots Teaching & Learning with Data in the Undergraduate Classroom Adam Beauchamp Christine Murray
- 2. Instruction Between Data Reference & Data Management • Data Reference – Locate statistics – Access data sets – Citation help • Presumes an understanding of what to do with data once discovered • Data Management – Project Planning – Metadata – Storage & Sharing • Presumes an understanding of the research process and data analysis
- 3. Novices in the Data Life Cycle Image: Research Data Services, University of Virginia Library
- 4. Pedagogical Models •Statistical literacy – Start with Exploratory Data Analysis (EDA) • Cobb & Moore, 1997 •History teaching – Heuristics of reading primary sources • Wineburg, 2001
- 5. Three Lesson Plans • Discovering Data Through the Literature – Modeling the experts • Evaluating Data Sets – Exploring data and formulating questions • Research Design – Operationalizing a research question
- 6. LESSON 1: DISCOVERING DATALESSON 1: DISCOVERING DATA THROUGH THE LITERATURETHROUGH THE LITERATURE
- 7. Sociological ResearchSociological Research MethodsMethods Students will be able to… •Find data from a citation •Understand the value of secondary data analysis •See the inextricable connection between data and the scholarly literature •Use documentation to evaluate a dataset Goal Students require an introduction to sources of quantitative data. The course is focused on attitudes toward immigration in the U.S. Library session •Mid-semester •45 minutes
- 8. Hoelter, L. F., Leclere, F. B., Pienta, A. M., Barlow, R. E., & McNally, J. W. (2008). Using ICPSR Resources to Teach Sociology. Teaching Sociology, 36(1), 17-25. doi: 10.1177/0092055X0803600103
- 10. Reading the Study Description • What is available, and how? • What is the universe, data collection period, and study methodology? • What variables are available?
- 11. Activity Search for one of the articles below by title or author in the ICPSR Bibliography of Data-Related Literature. Locate the dataset related to this article, and from the study page identify: •the year the data were collected •the universe of the study •a variable related to attitudes toward immigration Alba, R., Rumbaut, R.G., & Marotz, K. (2005). A distorted nation: Perceptions of racial/ethnic group sizes and attitudes toward immigrants and other minorities. Social Forces, 84(2), 901-919. doi: 10.1353/sof.2006.0002
- 12. Reflections • Relies heavily on ICPSR, to the exclusion of other data sources • Citations are provided, rather than arising out of the students’ own research • May emphasize relying on some highly used datasets, rather than seeking out novel sources
- 13. LESSON 2: EVALUATING DATA SETSLESSON 2: EVALUATING DATA SETS
- 14. Health and PopulationsHealth and Populations Students will be able to… •Understand the complex web of data producers •Use documentation to evaluate a dataset •Articulate how variables relate to research questions Goal Students in an introductory demography course must write a short paper based on their original data analysis, using a dataset of their choosing that relates to population dynamics, health disparities, etc. Library session •Mid-semester •90 minutes
- 15. Who cares? • Which organizations/entities care enough about this topic to collect data about it? • Who has the authority to collect data of this type?
- 16. What can the data show? • What will you use the data to investigate, and what variables will you need to do so? – a trend: a temporal variable – a disparity: different populations – a comparison: a geographic variable – a spatial pattern: smaller regions within a larger geographic area • How could this data be collected?
- 17. Activity • Small groups are assigned a pre-selected data source • By examining the documentation and the data itself, students report back on the characteristics of the data, and suggest possible questions the data could be used to answer
- 19. Reflections • For datasets not discussed, students must make the leap themselves between topics and possible data producers • The investigation starts with the data, rather the research question
- 20. LESSON 3: OPERATIONALIZATIONLESSON 3: OPERATIONALIZATION
- 21. SOCI 3040:SOCI 3040: Research AnalysisResearch Analysis Students will be able to… •Identify potential collectors and disseminators of data. •Describe the accessibility issues associated with data sources •Operationalize a research question in order to develop a data search strategy Group Project Present a research proposal that uses pre-existing quantitative data to test a hypothesis relevant to sociology. Library session •Early in semester •75 minutes •All sociology majors have library session focused on the literature review in previous course (prerequisite)
- 22. Part 1: Collectors of Data Who collects data? • As a class, students suggest potential creators of data. Is it accessible to us? • For each “collector,” students consider reasons to provide or to block access. Photo: U.S. Census Bureau
- 23. Part 2: Operationalization Group Exercise •What variables can you use to measure gentrification in New Orleans? •Where might you find this data? Photo: Infrogmation, New Orleans
- 24. Part 3: Data Search • Introduce LibGuide of Data Sources • Allow time for student groups to work on their projects Photo: TrekCore
- 25. Reflections Thoughts •Students will benefit from more practice with operationalization •Instructor collaboration critical to effective integration & reinforcement Assessments •Formative assessments •One-minute paper •Professor feedback
- 26. Bibliography Cobb, George W. and David S. Moore. 1997. “Mathematics, Statistics, and Teaching.” The American Mathematical Monthly 104(9):801–23. Hoelter, L. F., Leclere, F. B., Pienta, A. M., Barlow, R. E., & McNally, J. W. (2008). Using ICPSR Resources to Teach Sociology. Teaching Sociology, 36(1), 17-25. doi: 10.1177/0092055X0803600103 Wineburg, Samuel S. 2001. Historical Thinking and Other Unnatural Acts: Charting the Future of Teaching the Past. Philadelphia: Temple University Press.
- 27. Image Credits Infrogmation of New Orleans. 2008. “BywaterKeepOffHipstersStepsB.jpg.” Wikimedia Commons. Available at http://commons.wikimedia.org/wiki/File:BywaterKeepOffHipstersStepsB.jpg. Accessed 9 March 2015. Research Data Services, University of Virginia Library. “Steps in the Data Life Cycle.” Available at http://data.library.virginia.edu/data-management/lifecycle. Accessed 2 March 2015. TrekCore. “Elementary, Dear Data, No. 130.” Star Trek The Next Generation HD Screencaps (Season 2, episode 3). Available at http://tng.trekcore.com/hd/thumbnails.php?album=36. Accessed 9 March 2015. United States. Census Bureau. 2010. “1940 Census.” Slideshow. Available at http://www.census.gov/1940census/. Accessed 9 March 2015.
Editor's Notes
- Adam Beauchamp Research & Instruction Librarian (Social Sciences) Tulane University New Orleans, Louisiana [email_address] Christine Murray Social Science Librarian Bates College Lewiston, Maine [email_address]
- Data reference is probably the most familiar data service. Part of traditional reference services, helping users find statistical information, and more recently, data sets for secondary analysis. Statistical Abstracts and Census data; World Development Indicators. Attention now is largely focused on data management and data curation services, which have been among the top trends identified by ACRL in both 2012 & 2014. For us, asked to provide instruction with data in the library, the challenge is to translate some of the reference skills and resources into a pedagogically fruitful classroom setting for students who are new to the research endeavor and may have never used quantitative data before.
- Where to begin with the data novice? The data life cycle, a common feature of data management services and training, models the research process and shows how data management fits into familiar research processes. There are numerous other models, but I like this one from the University of Virginia because it adopts a user/researcher perspective and language, with descriptions on the website of data management steps associated with each step. This approach to data management services assumes the user understands the place of data in the research process, a perfectly reasonable assumption to make of faculty and experienced students. It also presents a logical sequence of events in the research process, from planning, to collection, to analysis, to communication. But does this work for the data novice? If you don’t know how to analyze quantitative data, how do you know what to look for or collect? This is not limited to undergrads. I had a grad student ask me for help finding data on how easy it is to start a business in West Africa. When I asked how would he measure this “ease” he looked at me confused. Having never done this before, he hadn’t thought about what kinds of data he needed to analyze, hence his difficulty in trying to find and collect them. Image credit: Research Data Services, University of Virginia Library. “Steps in the Data Life Cycle.” Available at http://data.library.virginia.edu/data-management/lifecycle. Accessed 2 March 2015.
- Pedagogical approaches from two different disciplines offer insight. First, from statistics education, Cobb and Moore suggest we begin with data analysis. Provide students with quantitative data, and teach them to interpret and ask questions of them. Once students are comfortable with exploring data, and have learned to recognize the potentials and limitations of any given dataset, then they can appreciate the need for careful design and collection techniques, and be able to think ahead to their analytical needs when planning for and implementing a data collection project. Note: EDA is a specific statistical teaching method developed by John Turkey in the 1970s, but we can adopt this approach in the library through a lens of hands-on exploration and brainstorming. We might also draw inspiration from history education. Sam Wineburg studied the different ways in which experts (historians) and novices (students) read primary source texts. The experts engage critically, considering the author’s intentions, audience reception, etc., while the students read for basic comprehension (as they’ve been taught to do). This led Wineburg to call for discipline- or context-specific teaching methods, implying students should learn how to engage with primary sources as a first step to learning history content, and certainly before being asked to locate and work with primary sources independently. References: Cobb, George W., and David S. Moore. 1997. “Mathematics, Statistics, and Teaching.” American Mathematical Monthly 104(9):801-823. Wineburg, Samuel S. 2001. Historical Thinking and Other Unnatural Acts: Charting the Future of Teaching the Past. Philadelphia: Temple University. Press.
- This lesson was developed for a sociology research methods course. Methods courses are important point for intervention for data skills and understanding.
- This lesson is in part a simplified version of Rachel Barlow’s “Exploring Data through Research Literature,” a resource available via ICPSR.
- The previous lesson illustrated how to approach data through the scholarly literature. This lesson, on the other hand, challenges students to think about where data come from.
- Learning objectives correlate to two class activities. Objectives 1 & 2 go together. The implied “in order to” for objective 1 is the same as for objective 3, develop a data search strategy. The implied “in order to” for objective 2 to establish realistic expectations. Objective 3 attaches to the second class activity.
- Sample of student responses, with reasons for sharing/not sharing data collected. Researchers: YES: institutional mandates (e.g. repositories), support research findings, contribute to collective knowledge NO: embargo until publication, not stored in accessible space (e.g. hard drives, personal files) Government YES: taxpayer funded, democracy, transparency NO: security, privacy NGOs, Think tanks, Other organizations YES: potential public mission, advocacy/persuasion NO: competitive advantage?, limited to subscribers only as revenue source (e.g. ACRL) Businesses YES: required disclosures (e.g. SEC filings) NO: competitive advantage, profits Photo credit: United States. Census Bureau. 2010. “1940 Census.” Slideshow. Available at http://www.census.gov/1940census/. Accessed 9 March 2015.
- In groups, students came up with potential variables that could be used to measure gentrification in neighborhoods. After allowing adequate time, they shared their findings with the class. Variables, which were listed on the board, included: House values, rents, crime levels, business in neighborhood (number and type). Household incomes, education levels, occupations (could indicate status). Bonus: # of hipsters – would require an operational definition of hipster, and perhaps observational strategy to count them. For each variable, consider whether this data is being collected and is available to us. Re: housing values, of interest to real estate industry, tax collecting gov’t entities, banks. Unless house is actively for sale, real estate data may be limited, and bank data likely unavailable for privacy and proprietary reasons. Gov’t data most likely to be available. Source of gov’t data in aggregate: Census (historic data available, but only in aggregate (block group or tract level)) Source of gov’t data for specific properties: Orleans Parish Tax Assessor’s office (only recent data) Photo credit Infrogmation of New Orleans. 2008. “BywaterKeepOffHipstersStepsB.jpg.” Wikimedia Commons. Available at http://commons.wikimedia.org/wiki/File:BywaterKeepOffHipstersStepsB.jpg. Accessed 9 March 2015.
- While students work on their group topics, the professor and I circulate and: Assist with operationalization Recommend data sources Encourage literature review search to generate ideas Photo credit TrekCore. “Elementary, Dear Data, No. 130.” Star Trek The Next Generation HD Screencaps (Season 2, episode 3). Available at http://tng.trekcore.com/hd/thumbnails.php?album=36. Accessed 9 March 2015.
- Formative assessments: Student participation during class & group activities Individualized assistance during data search time (offer clarifications on what remains unclear) One-minute paper: (one thing you learned, one thing that remains unclear, additional comments) Most note having learned about helpful sources of data Several note having learned about operationalization Professor feedback: Pleased with session Used the student feedback from one-minute paper to reinforce & review some concepts in next class meeting Professor has continued to come to the library for variations of this lesson plan Last two semesters I handed this over to her grad student TA (train the trainer)