VR Education Data Visualization

A comprehensive D3.js visualization analyzing Virtual Reality adoption in education, correlating engagement levels, usage hours, and assistive technology training availability across multiple datasets.

Overview

This project integrates and visualizes data from three distinct sources:

• VR Impact Dataset: Student VR usage, engagement, and learning outcomes
• VR Education Dataset: Student demographics, VR proficiency, and access to equipment
• Assistive Technology Dataset: Global training availability for assistive technologies by region

The main visualization is an interactive scatter plot that reveals correlations between VR engagement, usage hours, and assistive technology training availability.

Data Sources

1. Virtual_Reality_in_Education_Impact.csv

Student-level data tracking VR usage in educational settings.

Key Columns:

• Student_ID: Unique student identifier
• Age: Student age
• Grade_Level: Postgraduate, Undergraduate, High School
• Field_of_Study: Subject area (Science, Medicine, Arts, etc.)
• Usage_of_VR_in_Education: Yes/No indicator
• Hours_of_VR_Usage_Per_Week: Numeric hours
• Engagement_Level: 1-5 scale
• Improvement_in_Learning_Outcomes: Yes/No indicator
• Region: Geographic location (Europe, Asia, Africa, etc.)

2. Modified_Virtual_Reality_in_Education_Dataset.csv

Educational VR implementation data with institutional context.

Key Columns:

• Age: Participant age
• Grade_Level: Educational level
• Usage_of_VR_in_Education: Yes/No
• Hours_of_VR_Usage_Per_Week: Numeric hours
• Engagement_Level: 1-5 scale
• Access_to_VR_Equipment: Yes/No
• Stress_Level_with_VR_Usage: Low/Medium/High
• Instructor_VR_Proficiency: Beginner/Intermediate/Advanced

3. assistive technology.csv

Global assistive technology training data from WHO regions.

Key Columns:

• Location: Country name
• ParentLocation: WHO region (Europe, Americas, Eastern Mediterranean, etc.)
• Dim1: Training type (Cognition, Communication, Hearing, Mobility, Self-care, Vision)
• Period: Year of data collection (2021)
• Value: Indicator value

4. Virtual Reality Experiences.csv

User VR headset experience data.

Key Columns:

• UserID: Unique user identifier
• Age: User age
• VRHeadset: Device type (HTC Vive, Oculus Rift, PlayStation VR)
• Duration: Session duration in minutes
• MotionSickness: 1-10 scale
• ImmersionLevel: 1-5 scale

5. student_math_clean.csv & student_portuguese_clean.csv

Student academic performance and demographic data.

Key Columns:

• student_id: Identifier
• school: School code
• sex: Gender
• age: Student age
• parent_status: Living arrangement
• mother_education, father_education: Education level
• study_time: Hours per week
• class_failures: Count of failures

Visualization

Main Scatter Plot

Axes:

• X-axis: Engagement Level (1-5) or Number of Training Types (Assistive Technology)
• Y-axis: Hours of VR Usage Per Week (or Region Index for Assistive data)

Visual Encoding:

• Color: Dataset source
  • 🔵 Blue: Impact Data
  • 🟠 Orange: Education Data
  • 🟢 Green: Assistive Technology Data
• Shape: Data type
  • ⚫ Circles: Impact & Education students
  • 🔺 Triangles: Assistive Technology countries
• Fill Pattern: VR Usage Status (Impact & Education only)
  • Filled: Uses VR in education
  • Open/Hollow: Does NOT use VR in education

Interactive Features

• Hover Tooltips: Display detailed information for each data point
• Dynamic Highlighting: Points highlight on hover with increased stroke width
• Legend: Explains all visual encodings

Project Structure