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[PR #912] [CLOSED] Improve Introduction chapter based on student feedback #2949

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opened 2026-04-13 13:19:23 -05:00 by GiteaMirror · 0 comments
GiteaMirror commented 2026-04-13 13:19:23 -05:00
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📋 Pull Request Information

Original PR: https://github.com/harvard-edge/cs249r_book/pull/912
Author: @profvjreddi
Created: 7/31/2025
Status: ❌ Closed

Base: dev ← Head: improve-introduction-student-feedback

📝 Commits (1)

  • 8d49acf Improve Introduction chapter based on simulated student feedback

📊 Changes

1 file changed (+26 additions, -0 deletions)

View changed files

📝 book/contents/core/introduction/introduction.qmd (+26 -0)

📄 Description

Summary

This PR improves the Introduction chapter based on simulated classroom feedback from students with diverse backgrounds (systems, ML, embedded).

Motivation

Through classroom simulation using sub-agents representing different student personas, we identified key confusion points:

  • Systems students struggled with ML terminology (gradient descent, perceptron)
  • ML students struggled with systems concepts (deployment, infrastructure)
  • Embedded engineers concerned about resource constraints
  • All students needed better bridging between theory and practice

Changes

  1. Added Essential Terminology section with field-specific explanations:

    • For systems students: ML concepts using systems analogies
    • For ML students: Clear systems terminology
    • For embedded engineers: Resource constraints and edge ML

  2. Added concrete example (wake word detector) showing how ML systems engineering differs across deployment targets

  3. Added missing perceptron footnote with mathematical explanation

Testing

  • Improvements based on feedback from 20 simulated student personas
  • Addresses confusion severity scores of 4-5 (highest)
  • Helps students from all backgrounds understand core concepts

Impact

These changes make the textbook more accessible to the diverse student population taking ML Systems courses, where students come from systems, ML theory, embedded, and other backgrounds.

🤖 Generated with Claude Code

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/harvard-edge/cs249r_book/pull/912 **Author:** [@profvjreddi](https://github.com/profvjreddi) **Created:** 7/31/2025 **Status:** ❌ Closed **Base:** `dev` ← **Head:** `improve-introduction-student-feedback` --- ### 📝 Commits (1) - [`8d49acf`](https://github.com/harvard-edge/cs249r_book/commit/8d49acffb68f7f3e4b6f992fdaeabe1899ba5b0e) Improve Introduction chapter based on simulated student feedback ### 📊 Changes **1 file changed** (+26 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `book/contents/core/introduction/introduction.qmd` (+26 -0) </details> ### 📄 Description ## Summary This PR improves the Introduction chapter based on simulated classroom feedback from students with diverse backgrounds (systems, ML, embedded). ## Motivation Through classroom simulation using sub-agents representing different student personas, we identified key confusion points: - Systems students struggled with ML terminology (gradient descent, perceptron) - ML students struggled with systems concepts (deployment, infrastructure) - Embedded engineers concerned about resource constraints - All students needed better bridging between theory and practice ## Changes 1. **Added Essential Terminology section** with field-specific explanations: - For systems students: ML concepts using systems analogies - For ML students: Clear systems terminology - For embedded engineers: Resource constraints and edge ML 2. **Added concrete example** (wake word detector) showing how ML systems engineering differs across deployment targets 3. **Added missing perceptron footnote** with mathematical explanation ## Testing - Improvements based on feedback from 20 simulated student personas - Addresses confusion severity scores of 4-5 (highest) - Helps students from all backgrounds understand core concepts ## Impact These changes make the textbook more accessible to the diverse student population taking ML Systems courses, where students come from systems, ML theory, embedded, and other backgrounds. 🤖 Generated with [Claude Code](https://claude.ai/code) --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
GiteaMirror added the pull-request label 2026-04-13 13:19:23 -05:00
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