Chengchang Yu
Published on

📝 College Course Recommendation System：Deep Dive Analysis

Authors

🎯 The Core Problem

The Gap: University students face a daunting challenge - choosing the right courses from an overwhelming catalog to match their career goals. With limited guidance and unclear connections between academic courses and industry demands, students often make suboptimal decisions that hurt their employability after graduation.

The Paradox: More course options = More confusion (not better decisions)

Key Pain Points:

  • Exhaustive course lists with complex descriptions
  • Limited time to make informed decisions
  • Minimal guidance from peers or advisors
  • Disconnect between what universities teach and what industries need

💡 The Key Insight

The researchers applied Netflix-style recommendation algorithms to education, asking: "If streaming platforms can predict what movies you'll love, why can't universities predict which courses will advance your career?"

Their Breakthrough Approach:

  • Treat course selection like a personalization problem
  • Use real job market data (scraped from LinkedIn, Indeed, Seek) to identify in-demand skills
  • Extract skills from student resumes and compare them to industry requirements
  • Recommend courses that fill the specific skill gaps

The Formula:

Career-Ready Graduate = 
    (Current Skills + Recommended Courses) 
    → Industry-Required Skills

🔧 The Method: How It Works

Three-Layer Architecture

1️⃣ Frontend (User Interface)

  • Built with React and Figma for intuitive design
  • Students can:
    • Upload their resume (PDF)
    • Select completed courses
    • View personalized recommendations
    • See skill gap analysis

2️⃣ Backend (Processing Engine)

  • Firebase: Real-time database and authentication
  • PyPDF2: Extracts text from resumes
  • Lightcast API: Identifies technical skills from text
  • Google Cloud RAG Model: Generates personalized recommendations

3️⃣ Recommendation Engine (The Brain)

Uses Hybrid Filtering:

  • Content-Based: Matches course content to career goals
  • Collaborative: "Students like you also took..."
  • TF-IDF + Cosine Similarity: Measures relevance between courses and job requirements

📋 Real-World Example: How Sarah Uses the System

Sarah's Goal: Become a Data Scientist

Step 1: Sarah uploads her resume

  • System extracts: Python, SQL, Statistics

Step 2: System scrapes 100 Data Scientist job postings

  • Industry demands: Python, SQL, Machine Learning, Deep Learning, Cloud Computing, Data Visualization

Step 3: Skill Gap Analysis

  • ✅ Has: Python, SQL, Statistics
  • ❌ Missing: Machine Learning, Deep Learning, Cloud Computing, Data Visualization

Step 4: Course Recommendations

  1. COMP9417 - Machine Learning
  2. COMP9444 - Neural Networks & Deep Learning
  3. COMP9313 - Big Data Management
  4. COMP9321 - Data Services Engineering

Result: Sarah now has a clear, data-driven course selection strategy aligned with real job market demands.

🔑 The Technical Innovation

Keyword Extraction Methods Used:

  1. TF-IDF (Term Frequency-Inverse Document Frequency)

    • Identifies important words in course descriptions
    • Formula: TF-IDF = (Word frequency in document) × log(Total docs / Docs with word)

  2. RAKE (Rapid Automatic Keyword Extraction)

    • Fast, language-independent
    • Extracts key phrases, not just words
    • Great for multi-word skills like "machine learning"

  3. KeyBERT

    • Uses BERT embeddings for context-aware extraction
    • Understands "Python programming" vs "Python snake"

  4. Lightcast API

    • Industry-standard skill taxonomy
    • Recognizes 32,000+ skills
    • Updates with market trends

The RAG Model (Retrieval-Augmented Generation)

  • Combines retrieval (finding relevant courses) with generation (explaining why)
  • Provides explainable recommendations: "This course teaches TensorFlow, which appears in 78% of ML job postings"

📊 System Evaluation Results

User Satisfaction Metrics:

  • ✅ Skill Extraction Helpfulness: High positive response
  • ✅ Overall System Satisfaction: Users found recommendations relevant and actionable
  • ✅ Task Completion Rate: Students successfully uploaded resumes and received recommendations

Performance Metrics:

  • Response Time: Real-time recommendations (< 3 seconds)
  • Scalability: Handles multiple concurrent users via Firebase
  • Accuracy: Recommendations aligned with actual job requirements

⚠️ Current Limitations

  1. Technical Skills Only

    • Doesn't capture soft skills (teamwork, communication, leadership)
    • Missing: emotional intelligence, adaptability, creativity

  2. Limited Accessibility Features

    • No keyboard navigation for disabled users
    • No high-contrast mode for visually impaired

  3. Data Dependency

    • Relies on accurate job posting data
    • Quality depends on resume completeness

  4. Single University Focus

    • Currently designed for UNSW courses only
    • Needs adaptation for other institutions

🚀 Future Enhancements

Planned Improvements:

  1. Soft Skills Analysis

    • NLP models to extract leadership, communication from resumes
    • Recommend courses with team projects, presentations

  2. Multi-University Support

    • Expand course database beyond UNSW
    • Cross-institutional recommendations

  3. Career Path Visualization

    • Show "If you take these 3 courses → 85% match for Data Scientist role"
    • Interactive skill tree diagrams

  4. Real-Time Job Market Updates

    • Weekly scraping of job postings
    • Trending skills dashboard

  5. Accessibility Compliance

    • WCAG 2.1 AA standards
    • Screen reader support

🎬 One-Sentence Summary

This system bridges the gap between university education and industry demands by analyzing students' existing skills against real job market data, then recommending specific courses that fill identified skill gaps - essentially creating a "Netflix for course selection" that improves graduate employability through data-driven, personalized guidance.

🧒 The Simple Version

Imagine you're building a LEGO castle, but you don't know which pieces you're missing.

This system:

  1. Looks at the LEGO pieces you already have (your current skills)
  2. Checks what pieces you need for the castle (job requirements)
  3. Tells you exactly which LEGO sets to buy (courses to take)

Instead of guessing which courses help your career, the system shows you exactly what to study based on what companies are actually hiring for.

💭 Why This Matters

For Students:

  • ✅ Stop wasting time on irrelevant courses
  • ✅ Graduate with job-ready skills
  • ✅ Increase employability by 30-40%

For Universities:

  • ✅ Improve graduate outcomes
  • ✅ Better align curriculum with industry
  • ✅ Increase student satisfaction

For Employers:

  • ✅ Graduates arrive with relevant skills
  • ✅ Reduced training costs
  • ✅ Better talent pipeline

🔍 Key Takeaways for AI Builders

  1. Data Sources Matter

    • Real job postings > Generic skill lists
    • Fresh data > Outdated curriculum guides

  2. Hybrid > Pure Algorithms

    • Content-based + Collaborative > Either alone
    • Multiple keyword extraction methods > Single approach

  3. Explainability Builds Trust

    • "Why this course?" matters as much as "Take this course"
    • RAG models provide context, not just recommendations

  4. User Experience Drives Adoption

    • Iterative design with Figma prototypes
    • Simple upload → instant results workflow

  5. Scalability from Day One

    • Firebase handles growth automatically
    • Cloud-based APIs prevent bottlenecks

📈 The Business Model Potential

This system could be monetized as:

  • SaaS for Universities: $10K-50K/year per institution
  • Career Counseling Tool: $20-50/student/semester
  • Corporate Training: Adapt for employee upskilling
  • API Service: Sell skill-gap analysis to EdTech platforms

🤔 Critical Questions

  1. How do you handle rapidly changing tech skills?

    • AI/ML skills evolve every 6 months
    • Need continuous job market monitoring

  2. What about non-traditional career paths?

    • System assumes linear career progression
    • Entrepreneurship, freelancing need different models

  3. How do you prevent gaming the system?

    • Students might take "easy" recommended courses
    • Need difficulty/rigor weighting

  4. Privacy concerns?

    • Resume data is sensitive
    • Need clear data retention policies

This analysis is based on the research paper "Improving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests" published in arXiv:2511.09819v1 [cs.CL], November 2025.