Student Insomnia and Educational Outcomes Dataset

This dataset consists of 791 rows (responses) and 16 columns (features), focusing on the relationship between insomnia and its impact on educational outcomes. It includes self-reported data on sleep patterns, quality, fatigue, stress levels, academic performance, and lifestyle habits. The survey was conducted using Google Forms, ensuring broad accessibility and ease of participation.

Data Collection: The data was collected through an online survey administered via Google Forms in Oct-Nov 2024. Respondents were asked to provide insights into their sleep behaviors and the effects on their academic and daily activities.

Key Features:
1. Demographics: Year of study and gender.
2. Sleep Patterns: Frequency of difficulty falling asleep, hours of sleep, night awakenings, and overall sleep quality.
3. Cognitive and Academic Effects: Impact on concentration, fatigue, class attendance, assignment completion, and overall academic performance.
4. Lifestyle Factors: Electronic device usage before sleep, caffeine consumption, and physical activity frequency.
5. Stress Levels: Self-reported stress related to academic workload.

This dataset can be used for:
1. Machine learning analysis to model and predict academic performance based on sleep and lifestyle factors.
2. Statistical studies investigating the connection between sleep disturbances and educational outcomes.
3. Developing behavioral and educational interventions to improve student well-being and performance.

Format: The dataset consists of 16 columns in categorical or ordinal formats. It contains 791 rows with no missing data, making it ready for analytics and machine learning applications.

本数据集包含791行（回应）及16列（特征），聚焦于失眠与其对教育成果的影响之间的关系。数据集涵盖了关于睡眠模式、睡眠质量、疲劳感、压力水平、学术表现以及生活方式习惯的自我报告数据。调查通过Google Forms进行，确保了广泛的可访问性和参与便捷性。

数据收集：数据收集于2024年10月至11月通过在线调查（Google Forms）完成。受访者被要求提供关于其睡眠行为及其对学术和日常活动影响的相关见解。

关键特征：
1. 人口统计学：学习年份及性别。
2. 睡眠模式：入睡困难频率、睡眠时长、夜间觉醒次数以及整体睡眠质量。
3. 认知与学术影响：对专注力、疲劳感、课堂出勤、作业完成以及整体学术表现的影响。
4. 生活方式因素：睡前电子设备使用情况、咖啡因摄入量以及身体活动频率。
5. 压力水平：与学业负担相关的自我报告压力水平。

本数据集可用于：
1. 机器学习分析，以构建和预测基于睡眠及生活方式因素的教育成果模型。
2. 探究睡眠障碍与教育成果之间联系的统计分析。
3. 开发旨在改善学生福祉和表现的行為和教育教学干预措施。

格式：数据集包含16列，采用分类或序数格式。共791行，无缺失数据，适用于分析及机器学习应用。