VR Education Data Visualization

A comprehensive D3.js visualization platform analyzing Virtual Reality adoption in education, featuring interactive scatter plots, bar charts, and multi-dataset correlation analysis.

Overview

This project visualizes data from multiple sources to reveal patterns in VR engagement, usage hours, user demographics, and assistive technology training availability. The visualization platform provides two primary interactive views with dynamic filtering capabilities.

Features

• Interactive Dataset Filtering - Toggle between different data sources
• Multi-dimensional Analysis - Correlate engagement, usage hours, and demographics
• Dynamic Visualizations - Responsive charts that update based on user selections
• Color-coded Data - Visual encoding for different datasets and categories
• Hover Tooltips - Detailed information on demand
• Educational Focus - Insights tailored to understand VR in learning environments

Views

Engagement View

Interactive scatter plot analyzing the relationship between VR engagement levels and weekly usage hours.

Features:

• Toggle datasets: Impact and Education data
• Filter for VR users only or view all participants
• Hover tooltips showing detailed information
• Color-coded by dataset (Impact: blue, Education: orange)
• Visual distinction between VR users (solid circles) and non-users (hollow circles)
• Legend explaining encoding

Data Displayed:

• X-axis: Engagement Level (1-5 scale)
• Y-axis: Hours of VR Usage Per Week
• Point color: Dataset source
• Point fill: VR usage status

Users View

Grouped bar chart displaying user distribution across age groups and education levels.

Features:

• Filter by education level (All, High School, Undergraduate, Postgraduate)
• Grouped bars showing distribution within each age group
• Interactive bars with hover highlighting
• Color-coded by education level
• Clear legend identifying each education level
• Age groups in 5-year increments

Data Displayed:

• X-axis: Age Groups (5-year ranges)
• Y-axis: Count of Users
• Bar color: Education level
• Bar grouping: Multiple education levels per age group

Dataset Descriptions

1. Virtual_Reality_in_Education_Impact.csv

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

Key Columns:

• Student_ID - Unique student identifier
• Age - Student age range (12-30)
• EducationLevel - High School, Undergraduate, or Postgraduate
• Grade_Level - Educational grade level
• Field_of_Study - Subject area (Science, Medicine, Arts, Engineering, Business, Law, Education)
• Usage_of_VR_in_Education - Yes/No indicator
• Hours_of_VR_Usage_Per_Week - 0-13 hours
• Engagement_Level - 1-5 scale rating
• Improvement_in_Learning_Outcomes - Yes/No
• Instructor_VR_Proficiency - Beginner, Intermediate, or Advanced
• Region - Geographic region (North America, Europe, Asia, South America, Africa, Oceania)

Record Count: 49 students Use: Primary dataset for engagement analysis and learning outcome correlations

2. Modified_Virtual_Reality_in_Education_Dataset.csv

Educational VR implementation data with institutional context and infrastructure metrics.

Key Columns:

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

Record Count: 49 records Use: Users view user distribution analysis and infrastructure impact assessment

3. Virtual Reality Experiences.csv

User VR headset experience data measuring immersion and comfort metrics.

Key Columns:

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

Record Count: 49 experiences Use: Supplementary data for user experience analysis

4. Assistive Technology (assistive technology.csv)

Global assistive technology training availability data from WHO.

Key Columns:

• Location - Country name
• ParentLocation - WHO region
• Dim1 - Training type category
• Period - Year (2021)
• Value - Training availability indicator

Record Count: 50+ entries Use: Contextual data for assistive technology training availability analysis

5. Student Mathematics & Portuguese Datasets

Student demographic and performance data for math and Portuguese language courses.

Key Columns (Common):

• student_id - Unique identifier
• school - School identifier
• sex - Gender (M/F)
• age - Student age
• address_type - Urban/Rural
• family_size - Household composition
• parent_status - Living arrangement
• mother_education - Education level
• father_education - Education level
• travel_time - Commute duration
• study_time - Weekly study hours
• class_failures - Number of failures
• Performance scores (1-20 scale for math/Portuguese)

Record Count: 49 students each (math and Portuguese) Use: Cross-reference for student engagement and performance patterns

How to Use

1. View the Engagement Analysis:

   • Click the "Engagement" tab at the top
   • Toggle dataset visibility using the blue/orange dataset buttons
   • Click "VR Users Only" to filter for active VR users
   • Hover over points to see detailed information
   • Observe patterns between engagement and usage hours

2. Explore User Demographics:

   • Click the "Users" tab
   • Use education level filter buttons to focus on specific groups
   • Observe how user distribution varies across age groups
   • Compare education level representation

3. Interpret the Visualizations:

   • Solid circles = VR users; Hollow circles = Non-users
   • Point color indicates dataset source
   • Larger points or bars indicate more users/higher engagement
   • Tooltips provide specific values on hover

Project Structure