Data-Driven Student Success: Tracking Attendance, Stress & Risk Levels

 

Project Summary: Student Monitoring Dashboard

Objective

The dashboard is designed to monitor student well-being and performance using various metrics, such as attendance, stress levels, sleep patterns, and mood scores. The goal is to identify students at risk and improve intervention strategies.

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Dataset Overview

The student monitoring data consists of 15,000 records with the following key columns:

1. Student ID – Unique identifier for each student.
2. Date – Daily monitoring records.
3. Class Time – Scheduled study hours per day.
4. Attendance Status – Whether a student was Present, Late, or Absent.
5. Stress Level (GSR) – A physiological measure of stress.
6. Sleep Hours – Total sleep time per night.
7. Anxiety Level – A score indicating student anxiety levels.
8. Mood Score – A scale representing emotional well-being.
9. Risk Level – Classification of students into Low, Medium, or High Risk based on various factors.

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Key Insights & Dashboard Features

  Attendance Tracking – Identify students frequently late or absent.
  Stress & Anxiety Monitoring – Detect patterns of high stress/anxiety.
  Sleep & Mood Analysis – Assess the impact of sleep on mood and academic engagement.
  Risk Categorization – Highlight students who may need counseling or academic support.