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VR Experiences Scatter Plot

✓ Published1🌍 Public
KKal
Last edited Sep 29, 2025
Created on Sep 28, 2025

This interactive visualization explores Oculus Rift VR users by plotting motion sickness against age, with points colored by gender (blue for male, red for female, green for other). Users can toggle between the scatter plot and a histogram of immersion level distribution. Built with React and D3 v7, the visualization uses d3.scaleLinear for axes, d3.extent for domain calculation, and d3.csv for data loading from the Virtual Reality Experiences dataset.

AI-generated description

Virtual Reality in Education Impact

Description
This dataset explores how immersive learning with virtual reality (VR) compares to traditional teaching methods in educational settings. It captures student engagement, attention, and learning outcomes.

Source: Kaggle — Impact of Virtual Reality on Education by wagj786

Attributes and Types

  • ParticipantID → categorical (nominal). Unique identifier for each participant.
  • Age → quantitative (ratio). Participant's age in years.
  • Gender → categorical (nominal). Gender of the participant.
  • Group → categorical (nominal). Experimental group (VR vs Control).
  • SessionDuration → quantitative (ratio). Length of the learning session in minutes.
  • AttentionScore → quantitative (interval or ratio). Measured attention or focus metric.
  • ImmersionScore → quantitative (interval). Subjective or measured immersion level.
  • LearningGain → quantitative (ratio). Improvement in test scores (post minus pre).
  • DeviceType → categorical (nominal). Type of VR device used.
  • ReportedDiscomfort → ordered categorical. Levels such as None, Mild, Moderate, Severe.
  • ExperimentDate → temporal (date). The date when the session was run.

Visualization Ideas

  • Scatterplot: ImmersionScore vs LearningGain, colored by Group (VR vs Control).
  • Boxplot: AttentionScore across groups (VR vs Control).
  • Bar chart: Average ReportedDiscomfort by DeviceType.
  • Line chart: SessionDuration vs LearningGain over time.

Notes

  • Check for missing or incomplete participant data before analysis.
  • Normalize scores if combining metrics with different scales.
  • Encode ReportedDiscomfort as ordered values. Example: None = 0, Mild = 1, Moderate = 2, Severe = 3.
  • Convert ExperimentDate into proper datetime format for time based plots.
MIT Licensed

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