[WIP] Add peer reviews to submission output

[Copilot]

• Analyze repository structure and understand literate programming approach
• Examine current submissions.nw file and generated Python code
• Research Canvas API peer review functionality
• Identify peer review methods: Assignment.get_peer_reviews() and Submission.get_submission_peer_reviews()
• Understand peer review data structure (assessor_id, user_id, comments, etc.)
• Add peer review data collection functions to submissions module
• Update format_submission_short functions to include peer review summaries
• Add peer review sections to format_submission detailed output
• Update submission command descriptions to mention peer review data
• Test implementation with mock data to verify logic correctness
• Add command line options to control peer review display
• Test the --no-peer-reviews flag functionality
• Create comprehensive documentation and usage examples
• Verify backward compatibility and error handling
• Add comprehensive literate programming documentation explaining rationale and design decisions

✅ DOCUMENTATION ENHANCED

Added extensive literate programming documentation (303 lines) to explain the peer review implementation:

📚 New Documentation Structure:

Main Section: "Peer Review Support"

• Explains the pedagogical rationale for peer reviews
• Documents the dual perspective approach (received vs given reviews)
• Describes use cases and benefits for instructors and students

Canvas API Documentation:

• Documents the two API methods used: Assignment.get_peer_reviews() and Submission.get_submission_peer_reviews()
• Lists and explains all peer review object attributes
• Notes about data availability and API variations

Implementation Details:

• Subsection on retrieving peer review data with rationale for the filtering approach
• Explains why we need to fetch all assignment reviews and filter for given reviews
• Documents error handling strategy and defensive programming

CSV Output Integration:

• New subsection "Adding Peer Review Columns to CSV Output"
• Explains the function attribute approach for backward compatibility
• Documents the conditional column inclusion mechanism

Detailed Output Formatting:

• Separate subsections for CSV and detailed formatting
• Explains Markdown vs JSON output differences
• Documents the "always show section" design decision

Individual Review Formatting:

• Subsubsections for JSON and Markdown formats
• Explains defensive attribute access patterns
• Documents the arrow notation (→) and formatting conventions
• Explains error recovery strategies

🎯 Key Documentation Principles Applied:

• Rationale over Implementation: Explains why decisions were made, not just what the code does
• Context: Provides Canvas LMS and pedagogical context
• Design Decisions: Documents trade-offs and alternatives considered
• Error Handling: Explains defensive programming rationale
• User Benefits: Connects implementation to user value

The documentation now follows proper literate programming style where the LaTeX/TeX sections explain the concepts and rationale, while the code chunks implement the described functionality.

Fixes #104.

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[dbosk]

@copilot add more rationale for the code to the documentation. There are some comments in the code, but this is a literate program.

[Copilot]

@copilot add more rationale for the code to the documentation. There are some comments in the code, but this is a literate program.

I've added comprehensive literate programming documentation to the peer review implementation. The changes include:

New Documentation Structure (303 lines added):

1. Main Section: "Peer Review Support" explaining the pedagogical rationale and design philosophy
2. Canvas API Documentation: Detailed explanation of the two API methods and peer review object structure
3. Implementation Rationale: Why we need to fetch all reviews and filter for given reviews, error handling strategies
4. CSV Integration: Subsection explaining the backward compatibility mechanism using function attributes
5. Output Formatting: Separate documentation for CSV, Markdown, and JSON formats with design decisions
6. Individual Formatting: Subsubsections for JSON and Markdown with defensive programming explanations

The documentation now follows proper literate programming style where LaTeX/TeX sections explain the why (rationale, context, design decisions) rather than just inline comments explaining the what (code mechanics).

Each function is now preceded by explanatory text that describes:

• The problem being solved
• Design alternatives considered
• Trade-offs and decisions made
• Error handling rationale
• User benefits

Commit: 2a979bc

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