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Eighth Grade Guide

These daily lesson plans are designed to support educators embarking on their first year of teaching, offering a detailed resource book specifically tailored to their needs. We have selected code.org as our foundational resource, modifying its materials to suit a 9-week course structured for sixth graders. 8th-grade computer science curriculum organized into sequential units on cybersecurity foundations, data representation, and machine learning & AI. Each lesson is mapped to West Virginia and CSTA standards, spans one to two class periods, and includes bell-ringers, essential questions, materials lists, step-by-step activities, learning objectives, assessment methods, and special-ed accommodations. Early lessons engage students with hands-on explorations—tearing down computer hardware, encoding messages in binary, and creating pixel art—before building toward more complex skills, such as basic encryption, cross-tabulation, and big-data analysis. Mid-unit mini-projects (for example, designing a classmate recommendation engine) reinforce practical data science techniques, while later lessons introduce supervised versus unsupervised learning, bias evaluation through “model cards,” and numerical prediction models. The curriculum culminates in a multi-week capstone where students frame community-oriented problems, develop machine-learning solutions, and defend their models through presentations and peer feedback. Throughout, formative checks, peer presentations, and real-world applications ensure that students not only master technical content but also develop critical thinking, collaboration, and ethical reasoning skills.

Summary for 8th Grade Computer Science
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To access the curriculum guides for 8th grade, click on the following:
Excel Sheet
Book
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  • Unit 5: Data Representation & Foundations (Lessons 1–12, plus a mini project)

    • Lessons 1–4: Introduction to data, encoding/decoding, ASCII & binary, pixel-based image representation

    • Lessons 5–6: Combining multiple data formats; basic encryption/decryption

    • Lesson 7: Community-based data collection & analysis

    • Lessons 8–10: Data structuring, pattern interpretation (cross-tabulation), and simple algorithm design

    • Lesson 11: Big data concepts & analysis

    • Lesson 12: Intro to machine learning & AI fundamentals

    • Final mini-project (Lessons 13–15): Students design a class-mate–based recommendation engine, applying data collection, analysis, and simple algorithms Google Docs

  • Unit 7: Machine Learning & AI Applications (Lessons 16–25)

    • Lessons 16–17: Supervised vs. unsupervised learning; pattern recognition

    • Lessons 18–19: Classification models; creating “model cards” and bias evaluation

    • Lesson 20: Numerical prediction models

    • Lessons 21–22: Framing community-issue statements & applying ML models to real-world problems

    • Final capstone (Lessons 23–25): Presentation and defense of ML solutions, including model evaluation and recommendations Google Docs

  • Unit 1: Cybersecurity Basics (at least Lessons 1–2 shown)

    • Lesson 1: History and significance of cyber-attacks; importance of data protection

    • Lesson 2: Hands-on hardware/software exploration (disassembling computers to learn component functions)

    • (Additional cybersecurity lessons likely follow.) Google Docs

Key takeaways:

  • Standards alignment with both West Virginia’s Technology Standards and the CSTA framework.

  • A mix of theoretical concepts (e.g. “What is big data?” or “What is encryption?”) and hands-on activities (pixel art, building a recommendation engine, computer teardown).

  • Built-in formative checks (quizzes, peer presentations, worksheets) and accommodations for diverse learners.

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