4 Details for Teachers

4.1 Detailed Process Walkthrough

4.1.1 GitHub Organization Setup

To create your one-time organization, follow the steps below:

Go to https://github.com/settings/organizations
Click “New organization”
Choose organization name: UCL-[Department]-[Module]-[Year]
Set to “Private” initially
Add description and contact information

The automation handles all repository creation and configuration.

4.1.2 Assessment Preparation

Before running automation, prepare:

Issue Templates (automatically created):

The scripts create structured peer review templates with sections for:

Code quality assessment
Correctness evaluation
Constructive feedback
Learning reflections

Marking Criteria Integration:

Customize the issue template in github_functions.R to align with your specific marking criteria:

# Edit the peer_review_template variable to match your assessment goals

Assignment Instructions:

Prepare clear instructions that reference:

Learning objectives for peer review
Timeline and deadlines
Technical support resources
Assessment weighting (if applicable)

4.1.3 Student Data Collection

Collect GitHub usernames efficiently:

4.1.4 Student Data Collection

Collect GitHub usernames at the start of the module.

Option 1 — Moodle Survey
Create a Moodle survey with:

“GitHub username” text field
Instructions for creating GitHub accounts
Link to GitHub Education benefits

Option 2 — Submission Requirement

Include GitHub username as part of the assignment submission requirements.

Option 3 — Lab Session Collection

Collect usernames during the first computer lab session.

4.1.5 File Transfer Workflow

Automated Process (Recommended):

# Place files in submissions/[student_id]/ folders
# Run the main automation script
source("main_automation_script.R")

Manual Process (Backup Method):

If automation fails, you can still upload files manually: 1. Create repositories individually 2. Upload files through GitHub web interface 3. Set permissions manually 4. Generate assignment lists manually

4.1.6 Managing the Review Process

Monitoring Student Engagement:

# Run daily or weekly progress checks
source("monitor_progress.R")
run_progress_check()

# Generates:
# - Completion rate summaries
# - Individual student progress
# - Reminder emails for laggards

Facilitating Quality Reviews:

Monitor issue creation across repositories
Look for generic or unconstructive feedback
Highlight examples of excellent reviews to the class
Provide additional guidance if review quality is poor

4.1.7 Assessment Integration

Assessing Peer Review Participation:

Quantitative Measures:

Completion of required review templates
Timeliness of feedback submission
Engagement with issue discussions

Qualitative Assessment:

# Use monitoring script to extract review content
# Assess based on:
# - Constructiveness of feedback
# - Specificity of observations
# - Helpful suggestions provided
# - Professional communication tone

4.1.8 Troubleshooting with Automation

Common Issues and Solutions:

Script Failures:

# Check configuration
source("setup_config.R")
validate_configuration()

# Test GitHub connection
test_github_connection()

Student Access Problems:

Verify GitHub usernames in student_list.csv
Check repository permissions in GitHub organization
Use monitoring script to identify access issues

File Upload Issues:

Check file sizes (GitHub limit: 100MB per file)
Verify file paths in submissions folders
Review console output for specific error messages

Incomplete Reviews:

# Generate targeted reminders
source("monitor_progress.R")
run_progress_check()  # Creates reminder emails automatically

4.1.9 Advanced Features

Customizing Review Assignments:

Edit file_management.R to modify reviewer assignment logic:

# Change number of reviewers per student
assign_peer_reviewers(students, reviews_per_student = 3)

# Implement stratified assignment (e.g., by ability level)
# Custom logic can be added to the assign_peer_reviewers function

Integration with Continuous Assessment:

Use repository activity for participation grades
Track improvement over multiple assignments
Monitor collaborative discussion quality

Scaling for Large Classes:

The automation scripts handle classes up to 200+ students efficiently: - Batch processing minimizes API rate limits - Progress monitoring scales automatically - Email generation handles large volumes

4.1.10 Alternative Workflows

For Smaller Classes (<20 students):

Consider having all students review all submissions for maximum learning.

For Advanced Students:

Enable pull request reviews in addition to issues
Encourage code improvement suggestions
Allow collaborative debugging sessions

For Assessment-Heavy Modules:

Use multiple rounds of peer review
Implement peer review of peer reviews (meta-review)
Create portfolio-based assessment incorporating peer feedback

4.2 Evaluation and Improvement

4.2.1 Data Collection

Automated Metrics:

# Progress monitoring provides:
# - Completion rates
# - Engagement timing
# - Issue/PR creation patterns
# - Student interaction levels

Student Feedback:

Post-assignment surveys about GitHub experience
Focus groups on peer review value
Comparison with traditional feedback methods

Learning Outcomes Assessment:

Code quality improvement over semester
Development of collaborative skills
Professional tool proficiency

4.2.2 Iterative Improvements

Based on Student Feedback:

Adjust issue template questions
Modify review assignment algorithms
Update student guidance materials
Refine technical support processes

Based on Teaching Observations:

Identify common student misconceptions
Adjust scaffold levels for GitHub introduction
Modify assessment weightings
Update marking criteria

4.3 Resource Requirements

4.3.1 Technical Infrastructure

GitHub Organization: Free for educational use
Automation Scripts: Provided, no additional cost
R/RStudio: Free, open-source software
Computing Resources: Minimal - runs on standard academic workstations

4.3.2 Time Investment

Initial Setup (First Time):

Script configuration: 30 minutes
GitHub organization setup: 15 minutes
Student data preparation: 45 minutes
Total: ~1.5 hours

Per Assignment (Ongoing):

File organization: 15 minutes
Running automation: 5 minutes
Monitoring and reminders: 10 minutes weekly
Total: ~20 minutes + weekly monitoring

4.3.3 Support Requirements

For Students:

Provide comprehensive student guide
Offer optional GitHub tutorial sessions
Establish peer mentoring system
Create backup support during lab sessions

For Teaching Staff:

Train additional instructors on script usage
Establish backup procedures for technical failures
Document customizations and local adaptations
Plan for software updates and maintenance

4.4 Success Metrics and Expectations

4.4.1 Short-term Indicators

>90% successful repository setup
>80% completion of peer reviews within deadline
<5% technical support requests requiring instructor intervention
Positive student feedback on learning experience

4.4.2 Long-term Outcomes

Improved code quality in subsequent assignments
Increased student confidence with collaborative tools
Enhanced peer learning culture in the module
Transferable skills development for industry preparation

4.5 Emergency Procedures and Backup Plans

GitHub Service Outages:

Monitor GitHub status page
Extend deadlines if necessary
Use manual review process as fallback
Communicate clearly with students

Script Failures:

Revert to manual repository creation
Use provided troubleshooting guide
Contact technical support resources
Document issues for future improvement

Student Technical Difficulties:

Provide alternative access methods
Offer individual technical support
Consider accommodation for accessibility needs
Maintain flexible deadline policies

Remember: The automation scripts handle the complex technical setup, allowing you to focus on the pedagogical aspects of peer review and student learning outcomes. Start small with one assignment, gather feedback, and iterate based on your experience and student needs.