More Than Code

There's more to computing science than just code. These modules discuss the different social and cultural impacts of computing, and what can go wrong if hardware and software are not developed with care and respect for its possible users.

Lessons

Click on the dotted underline text to open each module, or click on the arrows to open a short summary of each module. Under each lesson that includes slides, you will find the corresponding speaker notes for you to follow along with

Lesson 1: Identity

Lesson 1 PDF

Only a small portion of an iceberg can be seen above the waterline. Similarly, understanding someone's identity is limited by what we see on the surface. This mini lesson explores the concept of identity and the importance of going "below the waterline" to challenge and avoid stereotypes and identity-based bias.

    Sections:
  1. Module 1: Define Identity
  2. Module 2: Understand importance of identity
  3. Module 3: Identity, Inclusion, and Belonging
  4. Module 4: Applying Identity and Inclusion

Lesson 2: Core Concepts [COMING SOON]

COMING SOON

Lesson 3: Diversity and Inclusion

Lesson 3 PDF
Speaker Notes
    This Lesson Teaches:
  1. Module 1: Define diversity and inclusion
  2. Module 2: Explain the benefits of diversity
  3. Module 3: Identify the difference between diversity and inclusion
  4. Module 4: Define computer science
  5. Module 5: Illustrate their understanding through the module activity

Lesson 4: Accessibility and Disability

Lesson 4 PDF

Disabilities can be visible or invisible and vary widely. The social model of disability focuses on removing societal barriers rather than fixing the individual. Language preferences (person-first vs. identity-first) and strong cultural identities are important within disability communities. Society often views disabilities negatively, leading to dehumanization and bullying. Resources for creating accessible technology include guidelines from the W3C and various accessibility initiatives.

Lesson 5: Racism and Ethnicity

Lesson 5 PDF
Speaker Notes

This module highlights racial bias in machine learning models across recruitment, word associations, facial recognition, and criminal justice, showing how these models often reflect historical human biases and misrepresentative data. Examples include gender bias in recruitment tools, stereotypical associations in language models, and facial recognition algorithms with lower accuracy for non-white individuals. Addressing these biases involves recognizing their causes and applying practical exercises to improve model fairness.

Lesson 6: LGBTQIA+ and Gender

Lesson 6 PDF
Speaker Notes

This module discusses how technology encodes assumptions and biases, particularly affecting LGBTQIA+ individuals. They cover definitions of sex, gender, and related complexities, highlight harmful assumptions, and provide historical examples of discrimination in STEM. The paradox of visibility and how technology can perpetuate existing inequities are explored, along with resources for further reading on gender and technology.

Lesson 7: Classism and Socioeconomic Status

Lesson 7 PDF

This module explores classism in college, focusing on motivations for attending college, the importance of independent versus interdependent skills, and the impact of socioeconomic status on academic performance. They include quizzes, a discussion of an article on college mobility culture, and suggestions for improving support for students from lower socioeconomic backgrounds.

Video Resources

Here are real scholars from around the world who deserve recognition for their contributions to STEM. They have broken barriers despite their identity and paved the way for others. Click on the scholars name below to be redirected to a video to learn more about who they are and what they have contributed.

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