Student Insomnia and Educational Outcomes Dataset

This dataset consists of 985 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 785 rows with no missing data, making it ready for analytics and machine learning applications.

本数据集共包含985条行数据（即有效回复）与16列特征，核心聚焦于失眠及其对教育成果的影响关联。数据集涵盖睡眠模式、睡眠质量、疲劳程度、压力水平、学业表现以及生活习惯的自我报告数据。本调研依托谷歌表单（Google Forms）开展，保障了调研的广泛可达性与参与便捷性。

### 数据采集
本数据集于2024年10月至11月期间，通过谷歌表单搭建的线上调研采集所得。调研要求受访者分享自身睡眠行为及其对学业与日常活动的影响情况。

### 核心特征
1. 人口统计学变量：就读学年与性别
2. 睡眠模式：入睡困难频率、每日睡眠时长、夜间觉醒次数以及整体睡眠质量
3. 认知与学业影响：对注意力、疲劳程度、课堂出勤率、作业完成情况以及整体学业表现的影响
4. 生活方式因素：睡前电子设备使用情况、咖啡因摄入情况以及体育活动频率
5. 压力水平：由受访者自我报告的与学业负荷相关的压力程度

### 数据集应用场景
1. 机器学习分析：基于睡眠与生活方式因素构建模型并预测学业表现
2. 统计研究：探究睡眠障碍与教育成果之间的关联机制
3. 干预方案开发：研发行为与教育干预手段，以提升学生的身心健康水平与学业表现

### 数据格式
本数据集包含16列分类或有序分类格式的特征，共包含785条无缺失值的行数据，可直接用于数据分析与机器学习建模工作。

搜集汇总

背景与挑战

背景概述

该数据集包含985名学生的自我报告数据，涵盖睡眠模式、认知影响、生活方式和压力水平等16个特征，用于研究失眠与教育成果的关系。数据通过在线调查收集，适用于机器学习和统计分析，旨在为改善学生健康和学习表现提供依据。