TeachingWithData.org ASA Presentation 2010
description
Transcript of TeachingWithData.org ASA Presentation 2010
TeachingWithData.org Resources for Teaching
Quantitative Literacy in the Social Sciences
John Paul DeWitt & Lynette HoelterUniversity of Michigan
ASA Annual Meeting, August 15, 2010
Presentation Outline:
• Introducing the project partners• Quantitative Literacy • Introducing TeachingWithData.org
– General overview (demo of Website)– Sociology-related resources– Future directions
Project Partners• ICPSR • SSDAN• Others involved:
– American Economic Association Committee on Economic Education
– American Political Science Association– American Sociological Association– Association of American Geographers– Science Education Resource Center, Carleton
College
ICPSR
• World’s oldest and largest social science data archive– Began in 1962 as ICPR
• Membership organization with 700+ members worldwide (non-members can use many resources)
• Summer Program in Quantitative Methods of Social Research
Current Snapshot of ICPSR• Currently 7,880 studies (65,200 data sets)
– Grouped into Thematic Collections– Available in multiple formats– Federal funding allows parts of the
collection to be openly available– Data sources:
• Government• Large data collection efforts• Principal Investigators• Repurposing• Other organizations
ICPSR: Undergraduate Education
• Fairly recent attention– Response to faculty– Undergrad users are fastest growing
segment
• Resources– OLC, SETUPS, ICSC, EDRL
• NSF-funded projects– TeachingWithData.org (NSDL)– Course, Curriculum, & Laboratory
Improvement project to assess the effect of using digital materials on students’ quantitative literacy skills
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SSDAN-OLC
• SSDAN’s primary focus is to assist in the dissemination of social data into the classroom with sites like DataCounts! and CensusScope
• ICPSRgreat track record in research, with a new attention on undergraduate education coming more recently with the welcomed Online Learning Center (OLC)
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SSDAN: Background• Started in 1995• University-based organization that creates
demographic media and makes U.S. census data accessible to policymakers, educators, the media, and informed citizens. – web sites– user guides – hands-on classroom materials
• Integrating Data Analysis (IDA)
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SSDAN: Classroom Products
• DataCounts! (www.ssdan.net/datacounts)– Collection of approximately 85 Data Driven Learning
Modules (DDLMs)– WebCHIP (simple contingency table software)– Datasets (repackaged decennial census and
American Community Survey)– Target audience is lower undergraduate courses
• CensusScope (www.censusscope.org)– Maps, charts, and tables – Demographic data at local, region, and national levels– Key indicators and trends back to 1960 for some
variables
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SSDAN: DataCounts!
Quickly connects users to datasets…
..or Data Driven Learning Modules
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SSDAN: DataCounts!
Menu for choosing a dataset for analysis
Brief List of available dataset collections
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SSDAN: DataCounts!Submitting a module:• Sections are clearly laid out• Forces faculty to create modules
with specific learning goals in mind.
• Makes re-use of module much easier
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SSDAN: DataCounts!
TitleAuthor and Institution
Brief Description
Faceted browsing to refine the search• Appropriate Grade Levels• Subjects (e.g. Family, Sexuality and
Gender)• Learning Time
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SSDAN: DataCounts!Data Driven Learning Modules are clearly laid out• Easy to read• Instructors can quickly identify
whether a module would be relevant to a specific course
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SSDAN: DataCounts!
• WebCHIPCommands for selecting variables, creating tables, graphing, and recoding
Basic information about the dataset
Running the “marginals” command shows the categories for each variable and frequencies
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SSDAN: DataCounts!
Students can quickly run simple cross tabulations to see distributions and test hypotheses
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SSDAN: DataCounts!
Controlling for an additional variable allows for deeper analysis
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SSDAN
• DataCounts!– Collection of approximately 85 Data Driven Learning
Modules (DDLMs)– WebCHIP (simple contingency table software)– Datasets (repackaged decennial census and
American Community Survey)– Target is lower undergraduate courses
• CensusScope– Maps, charts, and tables – Demographic data at local, region, and national levels– Key indicators and trends back to 1960 for some
variables
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SSDAN: CensusScope
New ACS data with improved look & feel coming Fall 2010
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SSDAN: CensusScope• Charts, Trends,
and Tables• All available for
states, counties, and metropolitan areas
Thinking about Quantitative Literacy (QL)
• CCLI project to measure effectiveness of using online modules to teach QL
• First need to agree on skill set representing QL in the social sciences– Most use data-based exercises to teach
content– QL/QR has gotten much recent attention
in institutional assessment, many schools requiring a QL component
What is QL?• “Statistical literacy, quantitative literacy, numeracy --
Under the hood, it is what do we want people to be able to do: Read tables and graphs and understand English statements that have numbers in them. That’s a good start,” said Milo Schield, a professor of statistics at Augsburg College and a vice president of the National Numeracy Network.
Shield was dismayed to find that, in a survey of his new students, 44 percent could not read a simple 100 percent row table and about a quarter could not accurately interpret a scatter plot of adult heights and weights.
Chandler, Michael Alison. What is Quantitative Literacy?, Washington Post, Feb. 5, 2009
Similar to Critical Thinking:
• Students as participants in a democratic society
• Skills include:– Questioning the source of evidence in a
stated point– Identifying gaps in information– Evaluating whether an argument is
based on data or opinion/inference/pure speculation
– Using data to draw logical conclusions
Quantitative Literacy
• Necessary for informed citizenry• Skills learned & used within a context• Skills:
– Reading and interpreting tables or graphs and to calculating percentages and the like
– Working within a scientific model (variables, hypotheses, etc.)
– Understanding and critically evaluating numbers presented in everyday lives
– Evaluating arguments based on data– Knowing what kinds of data might be useful in answering
particular questions
• For a straightforward definition/skill list, see Samford University’s (not social science specific)
Translating to Learning Outcomes
• Began with AAC&U rubric for quantitative reasoning• QL in social sciences:
– Calculation– Interpretation– Representation– Analysis– Method selection– Estimation/Reasonableness checks– Communication– Find/Identify/Generate data– Research design– Confidence
Learning Outcome Dimensions
• Calculation: Ability to perform mathematical operations
• Interpretation: Ability to explain information presented in a mathematical form (e.g., tables, equations, graphs, or diagrams)
• Representation: Ability to convert relevant information from one mathematical form to another (e.g., tables, equations, graphs or diagrams)
• Analysis: Ability to make judgments based on quantitative analysis
Learning Outcomes (con’t)
• Method selection: Ability to choose the mathematical operations required to answer a research question
• Estimation/Reasonableness Checks: Ability to recognize the limits of a method and to form reasonable predictions of unknown quantities
• Communication: Ability to use appropriate levels and types of quantitative information (data, reasoning, tools) to support a conclusion or explain a situation in a way that takes the audience into account.
Learning Outcomes (con’t)
• Find/Identify/Generate Data: Ability to identify or generate appropriate information to answer a question
• Research design: Understand the links between theory and data
• Confidence: Level of comfort in performing and interpreting a method of quantitative analysis
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Assessment Tools and Results
QL Skills Are Marketable
• Often cited by students as something “tangible” that they have learned
• Definable skill set useful in many career paths
• Easy to tie to everyday life
Including Data Builds QL and:
• Engages students with disciplines more fully – Active learning– Better picture of how social scientists
work– Prevents some of the feelings of
“disconnect” between substantive and technical courses
• Piques student interest• Opens the door to the world of data
TeachingWithData.org
• National Science Digital Library – only social science pathway
• Goal: Make it easier for faculty to use real data in classes– Undergraduate (esp. “non-methods”)– K(9)-12 efforts
• Includes survey of ~3600 social science faculty • Repository of data-related materials
– Exercises, including games and simulations– Static and dynamic maps, charts, tables– Data – Publications
• Tagged with metadata for easy searching
Major Changes since Oct. 2009
• Redesign of the interface on the main page– Guided Search from home page– Resources categorized by more general ‘resource type’ controlled vocabulary
• Data focused on tables and figures vs. data sets• Reference Shelf Data Sources, events, pedagogy• Classroom Resources Grouped like resources,
– Search box with grade level
• Spring Cleaning – removed hundreds of resources• Identified items at lower levels (higher granularity)• User log-in (OpenID) and submission• Local content• Data in the News blog• Data for Online Analysis• Reading list: ability to create, save, and share
– Favorites– List of resources for course, project, or textbook– TwD and external resources
New Account Setup (OpenID)
New Account Setup
TeachingWithData.org
TeachingWithData.org
TeachingWithData.org
TeachingWithData.org
Future Changes
• Professional Association editors– Submit, edit metadata, review resources
• “Report” button for review and edit– Cleaner metadata, outdated links, etc
• Comments• OpenStudy partnership?
– Ratings– Recommendations– User Collaborations (Instructor-Instructor, Instructor-
Student)– Instant feedback and help
– TRAILS indexing
OpenStudy.com
Sociology Resources
Example Resources
• “Data in the News” feature – good way to bring in current events
• Lesson plans/lectures• Data-driven exercises• Data sources• Tools
Lesson Plans (Example)
More Extensive Lesson Plans (Example)
International Data & Information for Comparison (Example)
Example: Short Video on Family Change in Canada
Static Tables (Example)
Interactive Maps (Example)
Data-Based Exercises: “Low-Tech” (Example)
Data-Based Exercises: Online (Example)
Data-Based Exercises: No Stat Software Needed (Example)
Simulations (Example)
Data for Online Analysis: No Software Needed (Example)
Educational Data Extracts for Statistics Packages (Example)
Tools for Data Visualization (Example)
Future Directions:
• Include resources for high school teachers
• Ability to link data to analysis and/or visualization tools
• Ability for faculty to rate and comment on resources
• Peer-reviewed materials and capability for faculty to upload their own resources
• Community building through professional associations and networks of users
Your Turn!
• What have you tried? • What has worked best? • Favorites we should include in TwD?
Acknowledgements
• PI: George C. Alter, ICPSR• Co-PI: William H. Frey, SSDAN
• Funded by National Science Foundation grant DUE-0840642