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.