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CSP Python — Unit 10: Data, Privacy & Ethical Computing (Lessons + Assessments)
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Description

Help students understand how computing impacts people, society, and the future.

Unit 10: Data, Privacy & Ethical Computing is a comprehensive Computer Science Principles unit that explores data privacy, algorithms, artificial intelligence, misinformation, ethics, and emerging technologies. This bundle includes all lessons AND assessments, making it a complete, ready-to-teach unit with no extra prep required.

The unit is worksheet-heavy, discussion-driven, and admin-friendly, perfect for CSP classrooms that want meaningful engagement without heavy coding.

📚 WHAT’S INCLUDED IN UNIT 10

📘 LESSONS (10.1–10.6)

Lesson 10.1 — Data Collection & Privacy
Students identify personal data, analyze privacy risks, and evaluate responsible data use.

Lesson 10.2 — Algorithms & Bias
Students explore algorithmic bias and analyze fairness in automated systems.

Lesson 10.3 — Artificial Intelligence & Automation
Students examine AI applications, benefits, risks, and automation’s role in society.

Lesson 10.4 — Social Media, Misinformation & Echo Chambers
Students analyze misinformation, algorithm influence, and media literacy.

Lesson 10.5 — Ethical Decision Making in Computing
Students apply ethical reasoning, evaluate consequences, and justify decisions.

Lesson 10.6 — The Future of Computing
Students explore emerging technologies, computing careers, and future trends.

Each lesson includes:

  • Student worksheet
  • Guided notes & vocabulary
  • Scenario analysis or reflection
  • Teacher guide with pacing and answer key

🧪 ASSESSMENTS

Quiz A — Lessons 10.1–10.3
Data, Algorithms & AI

  • 50-point assessment
  • Multiple choice, short answer, and scenarios

Quiz B — Lessons 10.4–10.6
Ethics, Media & the Future of Computing

  • 50-point assessment
  • Conceptual and ethical reasoning

Unit 10 Mini Project — Ethical Computing

  • Capstone-style project (50 points)
  • Real-world scenarios
  • Stakeholder analysis
  • Ethical justification
  • Python-style logic (conceptual)

Teacher Guides for All Assessments

  • Answer keys & sample responses
  • Flexible grading notes
  • Clear scoring guide for the mini project

🧠 TOPICS COVERED

  • Data collection & privacy
  • Algorithmic bias & fairness
  • Artificial intelligence & automation
  • Social media & misinformation
  • Ethical decision making
  • Emerging technologies & careers

🧑‍🏫 PERFECT FOR

  • High school Computer Science Principles (Grades 9–12)
  • Python-based CSP courses
  • Digital citizenship & ethics units
  • Semester 2 curriculum pacing
  • Sub plans and admin-reviewed lessons
Report this resource to TPT
Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

CSP Python — Unit 10: Data, Privacy & Ethical Computing (Lessons + Assessments)

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Highlights

Digital downloads
Grades icon
Grades
9th - 12th, Adult Education, Higher Education
Standards icon
Standards
Answer Key
Included with rubric
Teaching Duration
3 Weeks

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Description

Help students understand how computing impacts people, society, and the future.

Unit 10: Data, Privacy & Ethical Computing is a comprehensive Computer Science Principles unit that explores data privacy, algorithms, artificial intelligence, misinformation, ethics, and emerging technologies. This bundle includes all lessons AND assessments, making it a complete, ready-to-teach unit with no extra prep required.

The unit is worksheet-heavy, discussion-driven, and admin-friendly, perfect for CSP classrooms that want meaningful engagement without heavy coding.

📚 WHAT’S INCLUDED IN UNIT 10

📘 LESSONS (10.1–10.6)

Lesson 10.1 — Data Collection & Privacy
Students identify personal data, analyze privacy risks, and evaluate responsible data use.

Lesson 10.2 — Algorithms & Bias
Students explore algorithmic bias and analyze fairness in automated systems.

Lesson 10.3 — Artificial Intelligence & Automation
Students examine AI applications, benefits, risks, and automation’s role in society.

Lesson 10.4 — Social Media, Misinformation & Echo Chambers
Students analyze misinformation, algorithm influence, and media literacy.

Lesson 10.5 — Ethical Decision Making in Computing
Students apply ethical reasoning, evaluate consequences, and justify decisions.

Lesson 10.6 — The Future of Computing
Students explore emerging technologies, computing careers, and future trends.

Each lesson includes:

  • Student worksheet
  • Guided notes & vocabulary
  • Scenario analysis or reflection
  • Teacher guide with pacing and answer key

🧪 ASSESSMENTS

Quiz A — Lessons 10.1–10.3
Data, Algorithms & AI

  • 50-point assessment
  • Multiple choice, short answer, and scenarios

Quiz B — Lessons 10.4–10.6
Ethics, Media & the Future of Computing

  • 50-point assessment
  • Conceptual and ethical reasoning

Unit 10 Mini Project — Ethical Computing

  • Capstone-style project (50 points)
  • Real-world scenarios
  • Stakeholder analysis
  • Ethical justification
  • Python-style logic (conceptual)

Teacher Guides for All Assessments

  • Answer keys & sample responses
  • Flexible grading notes
  • Clear scoring guide for the mini project

🧠 TOPICS COVERED

  • Data collection & privacy
  • Algorithmic bias & fairness
  • Artificial intelligence & automation
  • Social media & misinformation
  • Ethical decision making
  • Emerging technologies & careers

🧑‍🏫 PERFECT FOR

  • High school Computer Science Principles (Grades 9–12)
  • Python-based CSP courses
  • Digital citizenship & ethics units
  • Semester 2 curriculum pacing
  • Sub plans and admin-reviewed lessons
Report this resource to TPT
Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

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Standards

to see state-specific standards (only available in the US).
Initiate and participate effectively in a range of collaborative discussions (one-on-one, in groups, and teacher-led) with diverse partners on grades 9–10 topics, texts, and issues, building on others’ ideas and expressing their own clearly and persuasively.
Cite specific textual evidence to support analysis of science and technical texts, attending to the precise details of explanations or descriptions.
Write arguments focused on discipline-specific content.
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