Grading Project Overview

[JoshThinh]

Automated Grading System for Computer Science Assignments

1. Checks the Code

Project Name: Automated Grading System for Computer Science Assignments
Objective:

• Develop a backend system that automates the grading of coding assignments.
• Leverage APIs to assess code correctness, quality, and submission punctuality.

• Provide continuous feedback to students using integrations with version control (GitHub).

2. Submission

• Automated Code Submission and Testing:

  • Accepts code submissions in multiple programming languages.
  • Executes code and tests it with predefined test cases.
  • Provides immediate feedback on test case success/failure.

• Grading Automation with Custom Rubrics:

  • Supports customizable grading rubrics (correctness, code quality, efficiency).
  • Grades based on passing test cases and code analysis tools (linters).

• Late Submission Penalty System:

  • Tracks submission timestamps and applies penalties for late submissions.
  • Instructors define penalty rules.

• Version Control Integration with GitHub:

  • Students submit GitHub repository links for version tracking.
  • Provides continuous feedback after each commit.
  • Offers feedback on version control practices (commit frequency, messages).

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3. System Workflow

1. Student Code Submission:

   • API accepts code uploads or GitHub repository links.
   • The code is passed to an execution environment via an external API (e.g., JDoodle).

2. Test Case Execution:

   • Submitted code is tested against predefined test cases.
   • Feedback is generated on the outcome of each test case.

3. Grading Process:

   • Rubrics define grading criteria (correctness, efficiency, style).
   • Linters (e.g., Pylint or Checkstyle) assess code quality.

4. Late Submission Penalty:

   • The system tracks submission timestamps.
   • Late penalties are applied based on predefined rules.

5. Continuous Feedback:

   • GitHub webhooks trigger regrading whenever new commits are pushed.
   • Feedback is updated continuously as the student improves the code.

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4. Technologies Used

• Backend Framework: Node.js/Express or Python/Flask
• Code Execution API: JDoodle or Sphere Engine
• Database: MongoDB or MySQL (storing submissions, grades, rubrics, penalties)
• Code Quality Tools: Pylint (Python) or Checkstyle (Java)
• Version Control Integration: GitHub API for repository submissions

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[JoshThinh]

Summary

Section 1: Code Submission and Execution API

• Allows submission of code in various programming languages.
• Executes the code and runs it against predefined test cases.

Section 2: Grading Automation with Rubric Integration

• Supports custom rubrics for different assignments.
• Grades based on correctness, style, and efficiency, with partial credit.

Section 3: Late Submission Penalty System

• Handles submission timestamps and applies late penalties automatically.
• Adjusts grades based on lateness.

Section 4: Continuous Feedback with GitHub Integration

• Integrates with GitHub for version control.
• Provides continuous feedback on code improvements with each new commit.