data literacy

Submitted by Sarah Hartman-Caverly on December 20th, 2023
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Short Description: 

The Hidden Layer Workshop introduces key generative AI (genAI) concepts through a privacy lens. Participants probe the possibilities and limitations of genAI while considering implications for intellectual privacy, intellectual property, data sovereignty, and human agency. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms in the neural networks underlying LLMs and their implications for machine bias and AI hallucination. Drawing on Richards’s theory of intellectual privacy (2015) and the movement for data sovereignty, and introducing an original framework for the ethical evaluation of AI, Hidden Layer prepares participants to be critical users of genAI and synthetic media.

The workshop is designed for a 60-minute session, but can be extended to fill the time available.
Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Attachments: 
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HiddenLayer_LessonPlan_CCBYSA_HartmanCaverly_2023.pdfdisplayed 1410 times117.63 KB
Learning Outcomes: 

Facilitator learning objectives

During this workshop, participants will

  • Apply prompt engineering techniques to elicit information from text-to-text generative AI (genAI) platforms

  • Appreciate a range of intellectual privacy implications posed by genAI, including: 

    • personal data;

    • intellectual property (copyright, patent, proprietary and sensitive data); 

    • AI alignment (social bias, content moderation, AI guardrails, censorship, prompt injection); 

    • synthetic media;

    • AI hallucination and mis/dis/malinformation; and

    • data sovereignty and data colonialism.

  • Engage in a simulation to develop a conceptual understanding of how the hidden layer in the neural networks underpinning large language models works

  • Synthesize their knowledge of genAI intellectual privacy considerations to analyze an ethical case study using the Agent-Impact Matrix for Artificial Intelligence (AIM4AI).

Participant learning outcomes

During this workshop, participants will

  • Interact with genAI to explore its possibilities and limitations

  • Discuss the intellectual privacy implications of genAI, including intellectual property considerations

  • Evaluate the ethics of genAI for its impact on human agency

Individual or Group:

Suggested Citation: 
Hartman-Caverly, Sarah. "Hidden Layer: Intellectual Privacy and Generative AI." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/hidden-layer-intellectual-privacy-and-generative-ai.
Submitted by Sarah Hartman-Caverly on August 4th, 2023
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Short Description: 

This workshop engages participants in exploring corporate data collection, personal profiling, deceptive design, and data brokerage practices. Workshop content is contextualized with the theoretical frameworks of panoptic sort (Gandy), surveillance capitalism (Zuboff), and the four regulators (Lessig) and presented through a privacy and business ethics lens. Participants will learn how companies make money from data collection practices; explore how interface design can influence our choices and behaviors; and discuss business ethics regarding privacy and big data.
The workshop is designed for 75-minute class sessions, but can be compressed into 60-minute sessions.
Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Attachments: 
AttachmentSize
DarkPatternsWorkshopLessonPlan_HartmanCaverly_CCBYNCSA.pdfdisplayed 1017 times84.44 KB
Learning Outcomes: 
  1. Learn how companies make money from data collection practices
  2. Explore how interface design can influence our choices and behaviors
  3. Discuss business ethics regarding privacy and big data.

Individual or Group:

Course Context (e.g. how it was implemented or integrated): 
Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 
Potential Pitfalls and Teaching Tips: 
Suggested Citation: 
Hartman-Caverly, Sarah. "Dark Patterns: Surveillance Capitalism and Business Ethics." CORA (Community of Online Research Assignments), 2023. https://projectcora.org/assignment/dark-patterns-surveillance-capitalism-and-business-ethics.
Submitted by Nicole Murph on April 20th, 2021
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Short Description: 

Reading charts and infographics is part of everyday life, yet telling a story with data can be tricky. Luckily, data visualization is a skill that everyone can learn! Data visualization is the practice of translating information into a visual context, helping humans understand complex concepts and making it easier to identify patterns and uncover insights. In this workshop, learn the basics of designing data visualizations, selecting appropriate graph styles, and how to identify misleading data visuals.

Attachments: 
AttachmentSize
Data Visualization Lesson Plan.docxdisplayed 798 times16 KB
Data Visualization Literacy Presentation.pdfdisplayed 3112 times2.93 MB
Data Visualization Workshop_Script.docxdisplayed 671 times29.73 KB
Misleading Visualization_Activity 1_Answer Key.docxdisplayed 730 times284.93 KB
Selecting A Visualization Type_Activity 2_Answer Key.docxdisplayed 794 times65.49 KB
Learning Outcomes: 

Students will understand inherent bias in data visualizations in order to be informed digital citizens.
Students will learn strategies to read and analyze data visualizations in order to meet their information needs.
Students will learn the elements of design in order to create appropriate visualizations.

Discipline: 
Multidisciplinary

Individual or Group:

Course Context (e.g. how it was implemented or integrated): 
Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 
Potential Pitfalls and Teaching Tips: 
Suggested Citation: 
Murph, Nicole. "Understanding Data Visualization." CORA (Community of Online Research Assignments), 2021. https://projectcora.org/assignment/understanding-data-visualization.
Submitted by Katrina Stierholz on December 12th, 2018
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Short Description: 

Students learn about innovation, the distribution of innovation across the country, and what can be patented. Working in groups, they examine patents and consider the changes the patents brought. They then use a mapping program and interpret data from that map to consider how local resources promote innovation.

Attachments: 
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Lesson plan (pdf)displayed 1109 times153.12 KB
Learning Outcomes: 

Students will be able to • define innovation, • define patents as protection of intellectual property, • explain how patents promote entrepreneurship, • interpret a map of patents assignments by county, and • explain the relationship between education, research institutions, and the frequency of patents and innovation.

Information Literacy concepts:

Additional Instructor Resources (e.g. in-class activities, worksheets, scaffolding applications, supplemental modules, further readings, etc.): 

Florida, Richard. “The Geography of Innovation.” Citylab blog post, September 2017; https://www.citylab.com/life/2017/08/the-geography-of-innovation/530349/

Assessment or Criteria for Success
Assessment Short Description: 
Assessment is in lesson plan.
Suggested Citation: 
Stierholz, Katrina. "Demonstrating the Distribution of Innovation and Entrepreneurship Using Patent Data and a Mapping Tool: GeoFRED® Marks the Spot." CORA (Community of Online Research Assignments), 2018. https://projectcora.org/assignment/demonstrating-distribution-innovation-and-entrepreneurship-using-patent-data-and-mapping.
Submitted by Katrina Stierholz on June 24th, 2018
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Short Description: 

A hybrid teaching module with two elements: an interactive online module for students to complete ahead of class and a face-to-face lesson plan that builds on the skills learned in the online lesson. The in-class session provides students with a critical exploration of the purchasing power of minimum wages across states and/or the earnings gap between men and women employed full time.

The pre-class online course is titled: “FRED Interactive: Information Literacy” available through www.econlowdown.org. In the online course, students review a FRED graph made in the course; define the concepts nominal, real, and inflation; and discuss basic strategies for establishing the reliability of a data source.

The in-person class lesson is titled: ACRL Information Literacy Frames as FRED-Integrated Abilities: The frames Research as Inquiry, Information Creation as a Process, Scholarship as Conversation, and Authority Is Constructed and Contextual are highlighted. The instructor has two possible tasks for students;
-Option A, students work in FRED and use the formula real = (nominal/CPI)*100 to plot inflation-adjusted minimum wage rates for two states and compare the results.
-Option B, students work in FRED to plot and compare nominal and real earnings differentials for men and woman.

The lesson includes a variety of in-class and out-of-class assessment activities and links to resources and a glossary of terms provide additional learning opportunities.

Attachments: 
AttachmentSize
Keeping_It_Real.pdfdisplayed 1060 times281.15 KB
Learning Outcomes: 

Students will:

Create
❏ New FRED® graphs

Define
❏ Minimum wage
❏ Nominal and real wages
❏ Consumer price inflation (CPI)

Identify
❏ Metadata in a FRED graph
❏ Additional questions for further research

Describe
❏ The frequency of data collection
❏ The components of a data citation
❏ The difference between data sources and aggregators
❏ The reasons for knowing how data are collected
❏ The difference between nominal and real wages
❏ The issues of authority regarding trustworthiness, reliability, and credibility of data sources

Individual or Group:

Suggested Citation: 
Stierholz, Katrina. "Keeping It Real: Teach ACRL Information Literacy Frames with FRED data ." CORA (Community of Online Research Assignments), 2018. https://projectcora.org/assignment/keeping-it-real-teach-acrl-information-literacy-frames-fred-data.
Submitted by Jeffrey Dowdy on June 11th, 2018
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Short Description: 

This session was part of an undergraduate, critical thinking and global perspectives course. The course is offered by various disciplines on campus. This instance focused on global challenges (The Seven Revolutions developed by csis.org). For the session the students applied two frameworks to data: authority is constructed and contextual and scholarship as conversation. Students learned about a data life cycle concept with emphasis on evaluation. One of the main goals in introducing the students to the life cycle of data (see attached) was to broaden their understanding of how to search for data. Students may encounter data via social media or in a magazine article. Often those formats are more accessible and present data in a way that is easier to understand. The exercise helps students to see how data can sometimes be manipulated in those formats, while also developing search techniques to track data to its source.

Attachments: 
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Slide 2 gives an illustration of the data lifecycledisplayed 1081 times663.82 KB
Learning Outcomes: 

Employ credible resources in studying key global challenges

Individual or Group:

Course Context (e.g. how it was implemented or integrated): 

The data literacy session was part of a semester-long, scaffolded research paper on a specific global challenge. Students were encouraged to use data to back up their arguments and research.

Assessment or Criteria for Success
Assessment Short Description: 
One issue with the assignment in its present form: students misunderstood the first question of the assignment (Provide two examples of types of data that inform your research topic). Many interpreted 'types' to mean actual data sets or reports instead of brainstorming about what data could exist. One hurdle students face, as Daniel Russell research scientist for Google would put it, is understanding how search works and what it can do for them. Students must know what questions they can ask. The first question was intended to help them think about what they could ask. This will require more modeling in the introduction.
Potential Pitfalls and Teaching Tips: 

For future iterations of this topic, I would like for students to evaluate multiple examples of data used in journalistic writing. Both to understand how to write with data and to see how data can be employed to make a point or to support a story.

Suggested Citation: 
Dowdy, Jeffrey. "Data Literacy." CORA (Community of Online Research Assignments), 2018. https://projectcora.org/assignment/data-literacy.

Teaching Resource

Collection of online tutorials from Arizona State University Library. Includes tutorials on citation styles, plagiarism, finding sources, database searching, and more.

Teaching Resource

The resources included represent 12 data-driven assignments created by USC faculty recipients of the Provost's Data-Driven Assignment Grant Program in Spring 2015.

Teaching Resource

The working space for the Institute of Museum and Library Services (IMLS) funded research project investigating data information literacy (DIL) needs of e-scientists.

Teaching Resource

The ETD+ Toolkit is an approach to improving student and faculty research output management.

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