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<p>This experimental study examined the impact of feature extraction on predicting semester integrated performance assessment (IPA) in virtual reality (VR) co-creation learning environments. A total of 83 students attended an 18-week-long VR co-creation course at a northern Taiwan public high school using a blended online-merge-offline (OMO) model. Evidence-based multimodal data, including log data, assessment scores, electroencephalography (EEG), and demographics, were used for dual-phase and single-phase Random Forest (RF) models which predicted binary pass/fail semester IPA. The dual-phase RF model, with PCA for feature extraction, demonstrated superior accuracy compared to the single-phase model; however, the single-phase model still achieved acceptable performance. Additionally, both RF models achieved high accuracy in predicting outcomes using EEG data, highlighting the potential of integrating physiological responses into educational settings. Further, prior knowledge and age emerged as crucial demographic features, whereas gender showed minimal influence on prediction accuracy. The study indicates limitations such as the scarcity of diverse cognition data, the lack of performance dashboards, and the absence of longitudinal analysis. Future studies should expand cognition data diversity, develop real-time algorithm-driven dashboards, and employ longitudinal studies to explore growth and community dynamics in VR co-creation.</p>

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Performance prediction in VR co-creation learning environments: Dual-phase vs. single-phase feature extraction and predictor evaluation in student engagement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Hsin-Yun Wang,
  • Jerry Chih-Yuan Sun

摘要

This experimental study examined the impact of feature extraction on predicting semester integrated performance assessment (IPA) in virtual reality (VR) co-creation learning environments. A total of 83 students attended an 18-week-long VR co-creation course at a northern Taiwan public high school using a blended online-merge-offline (OMO) model. Evidence-based multimodal data, including log data, assessment scores, electroencephalography (EEG), and demographics, were used for dual-phase and single-phase Random Forest (RF) models which predicted binary pass/fail semester IPA. The dual-phase RF model, with PCA for feature extraction, demonstrated superior accuracy compared to the single-phase model; however, the single-phase model still achieved acceptable performance. Additionally, both RF models achieved high accuracy in predicting outcomes using EEG data, highlighting the potential of integrating physiological responses into educational settings. Further, prior knowledge and age emerged as crucial demographic features, whereas gender showed minimal influence on prediction accuracy. The study indicates limitations such as the scarcity of diverse cognition data, the lack of performance dashboards, and the absence of longitudinal analysis. Future studies should expand cognition data diversity, develop real-time algorithm-driven dashboards, and employ longitudinal studies to explore growth and community dynamics in VR co-creation.