Automated Lab Submission & Integrity Checker (Python OOP)

🧩 Introduction

This project was developed as a Python final project in May 2025.
It automates the process of managing student lab submissions — renaming files, verifying submission integrity, and generating analytical reports.

It originated from a real problem in college courses:
instructors often receive hundreds of student .docx submissions with inconsistent naming conventions, misplaced files, and even duplicate or cross-referenced content.
The goal was to design a robust, automated, and object-oriented solution.

🧰 File Management

1. Standardizing file names with course code and student information
2. Matching student IDs with class roster sequence numbers for organized grading
3. Ensuring consistent file naming across all submissions

🔍 Content Analysis

1. Detecting shared content between submissions by identifying student IDs
2. Supporting both plagiarism detection and group work verification:
   • Identifying unauthorized sharing in individual assignments
   • Extracting team member information in group projects
3. Generating detailed cross-reference reports for instructor review

🗂 Demo Directory Structure

├── project_main.py                    Main program file
├── student_roster.csv                 Roster file with anonymized sequence numbers
│
├── sample_submissions/                Source directory for anonymized submissions
│   ├── Lab1_AAAA0001_COURSECODE_.docx
│   ├── BBBB0002_COURSECODE_Lab2.docx
│   ├── COURSECODE_Lab3_CCCC0003.docx
│   └── ... (other anonymized submission files)
│
└── output_processed/                  Directory for renamed and analyzed files
    ├── COURSECODE_A1_01_AAAA0001.docx   (Renamed with sequence number)
    ├── COURSECODE_A1_02_BBBB0002.docx   (Standardized naming format)
    ├── report_check.txt                 (Cross-reference report)
    └── file_summary.txt                 (Processing summary)

🧩 Key Features

├── Class: Student
│   ├── __init__():                     Initialize student object with basic information
│   ├── parse_name():                   Parse full name into first and last name
│   └── generate_condor_id():           Generate Condor ID from classlist.csv information
│
├── Class: FileChecker
│   ├── __init__():                     Initialize checker with directories and course info
│   ├── load_student_data():            Load and map student roster sequence numbers
│   ├── extract_id_from_filename():     Extract student ID from various filename formats
│   ├── check_file_content():           Identify other student IDs in file content
│   ├── process_files():                Rename files with roster sequence numbers
│   ├── generate_check_report():        Create content cross-reference analysis
│   └── generate_file_list():           Summarize file processing results
│
└── Function: main()
    └── Program entry point and user interaction handling

---

🧠 Code Logic Example

🔹 ID Extraction via Regex

def extract_id_from_filename(self, filename):
    patterns = [r"([a-zA-Z]{4}\\d{4})", r"_([a-zA-Z]{4}\\d{4})[\\._]"]
    for pattern in patterns:
        match = re.search(pattern, filename.lower())
        if match:
            return match.group(1)
    return None

🔹 Cross-Reference Detection

def check_file_content(self, file_path, id_list):
    '''Scans a .docx file to find any other student IDs mentioned in the content.'''
    found_ids = set()
    try:
        doc = Document(file_path)
        for para in doc.paragraphs:
            for sid in id_list:
                if sid in para.text:
                    found_ids.add(sid)
    except Exception:
        pass
    return list(found_ids)

📄 Output Format

• Renamed files:
  COURSECODE_ASSIGNMENT_##_studentid.docx
  (represents the student’s sequence number from the class roster)

• Cross-reference report:
  Details of student ID mentions across files

• Processing summary:
  Complete file handling results

🧰 Technical Summary

Concept	Implementation
Language	Python 3.12
Paradigm	Object-Oriented Programming (OOP)
Libraries	os, re, csv, docx, collections
Techniques	Regex parsing, I/O automation, exception handling
Output Files	file_list.txt, check.txt
Data Source	Roster CSV + .docx submissions

🧭 Lessons Learned

Through this project, I gained hands-on experience with:

• Designing structured automation workflows using OOP principles
• Managing file systems and text data parsing
• Applying regex to real-world problems
• Handling errors and exceptions for robust operation

This project helped me understand how automation tools can transform tedious, error-prone tasks into efficient workflows.
It also strengthened my confidence in building real-world Python applications that merge practicality with clean design.

🔒 Notes on Data Privacy

All student names, IDs, and course codes have been anonymized for public release.
Original data and institutional references were removed to ensure confidentiality and compliance.