地球资源数据云——数据资源详情
虚拟现实对教育的影响
该数据集《Impact of Virtual Reality on Education 》主要用于监督学习任务，数据形态以表格为主。 题目说明：Exploring the Transformative Effects of Virtual Reality on Learning Experiences 任务类型：表格监督学习。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：Virtual_Reality_in_Education_Impact.csv。 Welcome to the "Virtual Reality in Education Dataset," a comprehensive collection of data exploring how VR technology enhances the learning experience across various subjects. This dataset is designed to analyze the effectiveness of immersive learning in comparison to traditional teaching methods. Key Features: Educational Impact Metrics: Student Engagement Score (1 - 10): Measures how actively students participate in VR - based lessons compared to traditional ones. Retention Rate (%): Tracks knowledge retention levels after VR - enhanced learning sessions. Academic Performance (Grades): Compares students' test scores before and after VR integration. Subjects Covered: The academic subjects where VR was applied (e.g., Science, Mathematics, History). Session Duration (Minutes): The average time students spend in a VR learning session. Immersion Level (1 - 10): Rates the level of immersion experienced by students during VR - based learning. Teacher Feedback Score (1 - 10): Measures educators' perceptions of VR’s impact on learning. Student Feedback Score (1 - 10): Reflects students' opinions on the effectiveness and engagement level of VR lessons. Use Cases & Applications: Analyzing the correlation between VR engagement and academic success Comparing traditional vs. VR - based learning effectiveness Identifying the most impactful subjects for VR integration Exploring how immersion levels affect student retention rates Building machine learning models to predict the effectiveness of VR in education
摘要概览
该数据集《Impact of Virtual Reality on Education 》主要用于监督学习任务，数据形态以表格为主。 题目说明：Exploring the Transformative Effects of Virtual Reality on Learning Experiences
任务类型：表格监督学习。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：Virtual_Reality_in_Education_Impact.csv。
Welcome to the "Virtual Reality in Education Dataset," a comprehensive collection of data exploring how VR technology enhances the learning experience across various subjects. This dataset is designed to analyze the effectiveness of immersive learning in comparison to traditional teaching methods.
Key Features: Educational Impact Metrics: Student Engagement Score (1 - 10): Measures how actively students participate in VR - based lessons compared to traditional ones. Retention Rate (%): Tracks knowledge retention levels after VR - enhanced learning sessions. Academic Performance (Grades): Compares students' test scores before and after VR integration.
Subjects Covered: The academic subjects where VR was applied (e.g., Science, Mathematics, History). Session Duration (Minutes): The average time students spend in a VR learning session. Immersion Level (1 - 10): Rates the level of immersion experienced by students during VR - based learning.
Teacher Feedback Score (1 - 10): Measures educators' perceptions of VR’s impact on learning. Student Feedback Score (1 - 10): Reflects students' opinions on the effectiveness and engagement level of VR lessons. Use Cases & Applications: Analyzing the correlation between VR engagement and academic success Comparing traditional vs.
VR - based learning effectiveness Identifying the most impactful subjects for VR integration Exploring how immersion levels affect student retention rates Building machine learning models to predict the effectiveness of VR in education