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 multiple sources to reveal correlations between VR engagement, usage hours, and assistive technology training availability. The main visualization is an interactive scatter plot with toggleable dataset visibility.

Data Sources

The visualization combines data from five datasets:

1. Virtual_Reality_in_Education_Impact.csv - Student VR usage and outcomes
2. Modified_Virtual_Reality_in_Education_Dataset.csv - Educational VR implementation
3. assistive technology.csv - Global assistive technology training data
4. Virtual Reality Experiences.csv - User VR headset experience metrics
5. student_math_clean.csv & student_portuguese_clean.csv - Student academic performance

Dataset Descriptions

1. Virtual_Reality_in_Education_Impact.csv

Student-level data tracking VR usage in educational settings with learning outcomes.

Key Columns:

• Student_ID - Unique student identifier
• Age - Student age (12-30)
• Gender - Gender identity
• Grade_Level - Postgraduate, Undergraduate, or High School
• Field_of_Study - Subject area (Science, Medicine, Arts, Engineering, etc.)
• Usage_of_VR_in_Education - Yes/No indicator
• Hours_of_VR_Usage_Per_Week - Numeric hours (0-13)
• Engagement_Level - 1-5 scale (1=Low, 5=High)
• Improvement_in_Learning_Outcomes - Yes/No indicator
• Subject - Specific subject taught
• Instructor_VR_Proficiency - Beginner, Intermediate, or Advanced
• Region - Geographic location (Europe, Asia, Africa, North America, South America, Oceania)

Record Count: 49 students

2. Modified_Virtual_Reality_in_Education_Dataset.csv

Educational VR implementation data with institutional context and infrastructure details.

Key Columns:

• Age - Participant age (12-30)
• Grade_Level - Educational level
• Usage_of_VR_in_Education - Yes/No
• Hours_of_VR_Usage_Per_Week - Numeric hours (0-18)
• Engagement_Level - 1-5 scale
• Improvement_in_Learning_Outcomes - Yes/No
• Instructor_VR_Proficiency - Beginner, Intermediate, or Advanced
• Access_to_VR_Equipment - Yes/No indicator
• Stress_Level_with_VR_Usage - Low, Medium, or High
• Collab - Collaboration indicator (Yes/No)

Record Count: 49 records

3. assistive technology.csv

Global assistive technology training availability data from WHO regions. Contains education and training offerings for various assistive technology categories.

Key Columns:

• IndicatorCode - ASSISTIVETECH_Q07
• Indicator - "Availability of education/training for assistive technology"
• ParentLocation - WHO region (EMR, AMR, EUR, AFR, WPR)
• Location - Country name
• Dim1 - Training category (Cognition, Communication, Hearing, Mobility, Self-care, Vision)
• Period - Year (2021)
• Value - Indicator status code

Record Count: 49 entries

4. Virtual Reality Experiences.csv

User VR headset experience data measuring motion sickness and immersion levels.

Key Columns:

• UserID - Unique user identifier (1-49)
• Age - User age (19-60)
• Gender - Gender (Male, Female, Other)
• VRHeadset - Device type (HTC Vive, Oculus Rift, PlayStation VR)
• Duration - Session duration in minutes (5.3-58.5)
• MotionSickness - 1-10 scale
• ImmersionLevel - 1-5 scale

Record Count: 49 experiences

5. student_math_clean.csv & student_portuguese_clean.csv

Student academic performance and demographic data for Math and Portuguese courses.

Key Columns:

• student_id - Unique identifier
• school - School code (GP)
• sex - Gender (M/F)
• age - Student age (12-18)
• address_type - Urban or Rural
• family_size - Family composition
• parent_status - Living arrangement
• mother_education, father_education - Parental education levels
• mother_job, father_job - Parental occupations
• study_time - Hours per week
• class_failures - Count of failures

Record Count: 49 records each

Visualization Components

Main Scatter Plot

An interactive scatter plot that combines data from all three primary datasets:

Axes:

• X-axis: Engagement Level (1-5) for Impact/Education data; Training Types Count (1-6) for Assistive Technology data
• Y-axis: Hours of VR Usage Per Week for Impact/Education data; WHO Region for Assistive Technology data

Visual Encoding:

• Color by Dataset:
  • 🔵 Blue (#3277CC)** -mpact Data
  • 🟠 Orange: (#e66101)** -cation Data
  • 🟢 Green: Ass(#33a02c)** -tive Technology Data
• Sh ape: Da:ta Circles: Impa -t & Education students
  • 🔺 Triangles: Assist -ve Technology countries
• Fill Pattern: VR U:sa
  • Solid Fill - Uses VR in education (Impact/Education only)pen/Hollow**: Does N -T use VR in education

In

(Impact/Education only)# Interactive Features

• - Dataset Toggle Controls - Show/hide individual datasets or all at once r Tooltips: Displa - Dinformation for each data pnt
• Dyn on hoveramic Highlighting: Points -highlight on hoverncreed stroke visibility on mouseover
• **Color-Coded : Explai -s all visual encodings

Pro

• Responsive Layout - Adapts to container sizeject Structure