Student Dashboard with ML Analysis 📊

Project Overview

The Student Dashboard is an interactive web application built with Next.js that visualizes student performance and cognitive skills.

It combines:

• Data analysis and machine learning (Python / Colab notebook)
• Interactive charts and tables (Recharts in Next.js)
• Clustering students into learning personas

The dashboard helps teachers or analysts quickly understand student performance patterns and cognitive skill correlations.

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Live Demo

View the deployed dashboard here:
https://student-dashboard-sigma-seven.vercel.app/

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Project Structure

student-dashboard/

├─ app/

│ └─ page.js

├─ public/

│ └─ student_data.json

├─ analysis/

│ └─ Student_Dashboard_Analysis.ipynb

├─ package.json

├─ next.config.js

└─ README.md

Explanation:

• app/page.js → Contains the main dashboard layout, cards, charts, and table
• public/student_data.json → Sample student dataset used in the dashboard
• analysis/Student_Dashboard_Analysis.ipynb → Python notebook for correlation analysis, ML prediction, and clustering

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Features

1. Overview Section

• Displays average scores (assessment, comprehension, attention, focus, retention, engagement time)
• Quick statistics for overall student performance

2. Visualizations

• Bar Chart: Comprehension vs Assessment Score
• Scatter Chart: Attention vs Performance
• Radar Chart (optional): Student cognitive profile

3. Student Table

• Searchable and sortable
• Selecting a student updates charts dynamically

4. Machine Learning Insights

• Correlation analysis identifies which cognitive skills influence assessment scores most
• Linear Regression predicts assessment scores from cognitive skills
• KMeans Clustering groups students into learning personas

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Dataset Format

The dashboard uses student_data.json with the following columns:

student_id	name	class	comprehension	attention	focus	retention	assessment_score	engagement_time
Student_1	Alice	10A	80	75	70	85	78	120

You can replace it with your own JSON or CSV with the same column structure.

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Setup Instructions

1. Clone Repository

git clone https://github.com//student-dashboard.git

cd student-dashboard

2. Install Dependencies

npm install

3. Run Dashboard Locally

npm run dev

Open http://localhost:3000 in your browser

ML Analysis Notebook

Location: analysis/Student_Dashboard_Analysis.ipynb

Contains:

Data import and preprocessing

Correlation heatmap between cognitive skills and performance

Linear Regression model to predict assessment_score

KMeans Clustering for learning personas

Visualizations for clusters and skill analysis

Key Findings

Correlation Insights

Comprehension and Focus have the strongest positive correlation with assessment scores

Attention contributes moderately

ML Prediction

Linear Regression predicts assessment_score using cognitive skills

R² score shows model accuracy

Clustering

Students grouped into 3 clusters:

Cluster 0: High attention, moderate comprehension

Cluster 1: Balanced skills

Cluster 2: High comprehension & focus

Helps identify learning personas for personalized teaching strategies

Technologies Used

Frontend: Next.js, React, Recharts

Backend/Data: JSON/CSV dataset

Data Analysis / ML: Python, Pandas, Seaborn, scikit-learn

Deployment: Vercel

Author

Tharun. U

GitHub: https://github.com/Tharun515