📚 Personalized Learning Tracker

SQL-Based Course Progress & Learning Analytics System

SQL • Database Design • Data Analysis • Learning Analytics

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📌 Overview

Personalized Learning Tracker is a SQL-based database project designed to manage, track, and analyze learner progress across courses, modules, assignments, and learning activities.

The project models a structured educational platform database and provides SQL queries for analyzing course completion, assignment progress, learner performance, scores, and learning activity trends.

It demonstrates practical application of relational database design, SQL querying, data aggregation, joins, filtering, and learning analytics.

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🎯 Project Objectives

The system is designed to:

• Manage learner and instructor information.
• Organize courses and their modules.
• Track assignment-level learner progress.
• Manage course access permissions.
• Analyze assignment completion.
• Calculate learner progress percentages.
• Evaluate average learner performance.
• Identify learners requiring additional support.
• Analyze learning activity over time.

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🧩 Core Database Entities

The project is structured around the following entities:

Table	Purpose
users	Stores learner and instructor information
courses	Stores course details and ownership information
modules	Organizes course content into logical modules
assignments	Represents assignments associated with modules
progress	Tracks learner assignment completion and scores
permissions	Manages user access to courses

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🏗️ Database Relationship

The conceptual relationship between the major entities is:

                    ┌──────────────┐
                    │    users     │
                    └──────┬───────┘
                           │
             ┌─────────────┴─────────────┐
             │                           │
             ▼                           ▼
      ┌──────────────┐           ┌────────────────┐
      │ permissions  │           │    progress    │
      └──────┬───────┘           └───────┬────────┘
             │                           │
             ▼                           ▼
      ┌──────────────┐            ┌──────────────┐
      │   courses    │◄───────────│ assignments  │
      └──────┬───────┘            └──────┬───────┘
             │                           │
             ▼                           │
      ┌──────────────┐                   │
      │   modules    │◄──────────────────┘
      └──────────────┘

The structure supports analysis across multiple levels:

Learner
   ↓
Course
   ↓
Module
   ↓
Assignment
   ↓
Progress / Score

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📂 Repository Contents

Personalized-Learning-Tracker/
│
├── COM USERS.docx
├── COM courses.docx
├── COM modules.docx
├── perssion.docx
├── progress_inserts.sql
├── sql question.docx
├── kk.txt
└── README.md

File Description

File	Description
COM USERS.docx	User table structure and sample learner/instructor data
COM courses.docx	Course table structure and course data
COM modules.docx	Module table structure linked to courses
perssion.docx	Permission/access data for course users
progress_inserts.sql	SQL INSERT statements for learner progress data
sql question.docx	SQL queries for progress and performance analysis
kk.txt	Supporting project/reference content

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📊 Analytics Capabilities

The SQL query suite focuses on practical learning analytics, including:

1. Learner Course Progress

Calculates:

• Total assignments
• Completed assignments
• Course completion percentage
• Course-level learner progress

2. Learner Performance Analysis

Evaluates:

• Completed assignments
• Average scores
• Learner performance levels
• Learners requiring additional support

3. Learning Activity Trends

Analyzes assignment completion by time period to identify:

• Weekly learning activity
• Completion patterns
• Changes in learner engagement

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🔎 Example SQL Analysis

Overall Course Progress

SELECT 
    u.user_id,
    u.name,
    c.title AS course_title,
    COUNT(a.assignment_id) AS total_assignments,
    COUNT(p.assignment_id) AS completed_assignments,
    ROUND(
        COUNT(p.assignment_id) / COUNT(a.assignment_id) * 100,
        2
    ) AS completion_percentage
FROM users u
JOIN progress p 
    ON u.user_id = p.user_id
JOIN assignments a 
    ON a.assignment_id = p.assignment_id
JOIN modules m 
    ON m.module_id = a.module_id
JOIN courses c 
    ON c.course_id = m.course_id
WHERE u.user_id = 1
GROUP BY u.user_id, c.course_id;

This query evaluates a learner's progress within a specific course by comparing total assignments against completed assignments.

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Learner Performance Analysis

SELECT 
    u.user_id,
    u.name,
    COUNT(p.assignment_id) AS completed_assignments,
    AVG(p.score) AS average_score
FROM users u
JOIN progress p 
    ON u.user_id = p.user_id
GROUP BY u.user_id
HAVING COUNT(p.assignment_id) < 3
    OR AVG(p.score) < 50;

This analysis identifies learners with relatively low assignment completion or average performance.

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Weekly Learning Activity

SELECT 
    WEEK(p.completed_at) AS week_number,
    COUNT(*) AS completed_assignments
FROM progress p
WHERE p.user_id = 1342
  AND p.completed_at IS NOT NULL
GROUP BY WEEK(p.completed_at)
ORDER BY week_number;

This query analyzes assignment completion patterns across weeks for a selected learner.

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🛠️ Technical Skills Demonstrated

• SQL
• Relational Database Design
• Database Schema Modeling
• Complex SQL Joins
• Aggregation Functions
• GROUP BY and HAVING
• Filtering and Conditional Analysis
• Progress & Performance Analytics
• Learning Analytics
• Data Management

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📈 Business & Analytical Use Cases

The project demonstrates how structured learning data can support:

• Learner progress monitoring
• Course completion analysis
• Assignment performance tracking
• At-risk learner identification
• Learning engagement analysis
• Academic performance reporting
• Personalized learning insights
• Educational analytics

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💡 Key Analytical Questions

The database can be used to answer questions such as:

• How much progress has a learner made in a course?
• What percentage of assignments has a learner completed?
• Which learners have low average scores?
• Which learners may require additional academic support?
• How frequently is a learner completing assignments?
• How does learner activity change over time?
• Which courses and modules are associated with learner activity?

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🔮 Future Enhancements

Potential extensions include:

• Interactive SQL-powered analytics dashboard
• Learner performance scoring
• Automated at-risk learner identification
• Course completion forecasting
• Personalized course recommendations
• Advanced learner segmentation
• Power BI / Tableau integration
• Automated performance reports
• Stored procedures and views
• Advanced learning analytics

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📚 Learning Outcomes

This project demonstrates practical experience in:

• Designing relational database structures
• Working with normalized educational data
• Writing multi-table SQL queries
• Performing aggregation and analytical calculations
• Analyzing learner performance
• Extracting actionable insights from structured data
• Translating business questions into SQL queries
• Building a foundation for educational analytics systems

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👨‍💻 Author

Monesh R

Data Analyst | AI & Data Quality | Business Intelligence | SQL & Data Analytics

Focused on building practical solutions using:

SQL • Python • Data Analytics • Business Intelligence • Data Quality • Automation

LinkedIn:
https://linkedin.com/in/moneshr

GitHub:
https://github.com/Mo-nesh

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⭐ Project Support

If you find this project useful, consider giving the repository a ⭐ on GitHub.