ismail31415/uniGame

---
datasets:
- name: UniGame Dataset
task_categories:
- text-classification
license: mit
tags:
- machine learning
- data science
- mental health
pretty_name: UniGame
size_categories:
- n<1K
---

# UniGame Dataset

This dataset explores the relationship between gaming habits and academic performance among students. It includes various attributes such as age, educational level, CGPA, gaming habits, and other related factors.

## Dataset Details

### Dataset Description

This dataset aims to investigate how gaming affects the academic performance of students. It includes information on the respondents' demographics, gaming habits, and academic results.

- **Curated by:** Hossain et. al
- **Language(s):** English
- **License:** MIT

## Uses

### Direct Use

This dataset can be used for various purposes, including but not limited to:

- Analyzing the impact of gaming on academic performance.
- Studying the correlation between gaming habits and lifestyle factors.
- Developing machine learning models to predict academic performance based on gaming habits and other related factors.

### Out-of-Scope Use

This dataset should not be used for malicious purposes or any application that the dataset is not suitable for.

## Dataset Structure

### Files

The dataset consists of two files:

- `train.csv`: The training set.
- `test.csv`: The testing set.

### Columns

The dataset contains the following columns:

1. **What is your age?**: The age of the respondent.
2. **Current educational position?**: The educational level of the respondent.
3. **Gender?**: The gender of the respondent.
4. **Your current CGPA?**: The current CGPA of the respondent.
5. **Your Higher Secondary School(H. SC) or A level or equivalent result?**: The higher secondary school result of the respondent.
6. **At what age you had started playing games?**: The age at which the respondent started playing games.
7. **Do you play games on mobile or pc?**: The platform on which the respondent plays games.
8. **When you go to sleep?**: The time the respondent goes to sleep.
9. **Do you attend your morning class regularly?**: Whether the respondent attends morning classes regularly.
10. **The average time you spend playing games?**: The average time the respondent spends playing games.
11. **Do you play paid or non-paid games?**: Whether the respondent plays paid or non-paid games.
12. **How many time you spend with family and friend?**: The time the respondent spends with family and friends.
13. **How you fill when you can not play game in whole day?**: The respondent's feeling when they cannot play games for a whole day.
14. **How you fill to complete game level?**: The respondent's feeling when they complete a game level.
15. **If you didn't finish games last level what is your feeling?**: The respondent's feeling if they didn't finish the last level of a game.
16. **Do you fill Fatigue?**: Whether the respondent feels fatigue.
17. **Do you play games for stress relief?**: Whether the respondent plays games for stress relief.
18. **Are you wearing glasses?**: Whether the respondent wears glasses.

## Dataset Creation

### Curation Rationale

The dataset was created to understand the impact of gaming on students' academic performance and lifestyle. It aims to provide insights that can help educators and policymakers make informed decisions.

### Source Data

#### Data Collection and Processing

The data was collected through a structured questionnaire filled out by students. The responses were then processed to ensure consistency and accuracy.

#### Who are the source data producers?

The source data was produced by students who participated in the survey. Their responses were anonymized to protect their privacy.

### Annotations

#### Annotation process

No additional annotations were made to the dataset beyond the initial data collection.

#### Who are the annotators?

The respondents themselves provided the data.

#### Personal and Sensitive Information

The dataset contains information that might be considered personal, such as age, gender, and academic results. All data was anonymized to ensure the privacy of the respondents.

## Bias, Risks, and Limitations

Users should be aware of potential biases in the dataset, as it may not represent all student populations equally. The dataset should be used with caution, considering its limitations.

### Recommendations

Users should consider the biases, risks, and limitations of the dataset. It is recommended to use the dataset in conjunction with other data sources to ensure robust analysis.
## Usage
To load and use this dataset, you can use the following code snippets in Python:

### Loading the dataset with Hugging Face Datasets library

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("ismail31415/uniGame")

# Access the training and testing sets
train_df = dataset['train']
test_df = dataset['test']

原始信息汇总

UniGame Dataset

数据集描述

• 名称: UniGame Dataset
• 任务类别: 文本分类
• 许可证: MIT
• 标签: 机器学习, 数据科学, 心理健康
• 显示名称: UniGame
• 大小类别: n<1K

数据集详情

• 目标: 研究游戏习惯与学生学术表现之间的关系。
• 包含信息: 年龄、教育水平、CGPA、游戏习惯及其他相关因素。
• 语言: 英语
• 创建者: Hossain et. al

用途

• 直接用途:
  • 分析游戏对学术表现的影响。
  • 研究游戏习惯与生活方式因素之间的关联。
  • 开发基于游戏习惯和其他相关因素预测学术表现的机器学习模型。
• 超出范围的用途: 不应用于恶意目的或不适合的应用。

数据集结构

• 文件:
  • train.csv: 训练集。
  • test.csv: 测试集。
• 列:
  1. 年龄
  2. 当前教育水平
  3. 性别
  4. 当前CGPA
  5. 高中或同等学历成绩
  6. 开始玩游戏的年龄
  7. 游戏平台（手机或电脑）
  8. 睡觉时间
  9. 是否定期参加早课
  10. 平均游戏时间
  11. 游戏类型（付费或免费）
  12. 与家人和朋友相处的时间
  13. 无法全天玩游戏时的感受
  14. 完成游戏关卡时的感受
  15. 未完成游戏最后一关时的感受
  16. 是否感到疲劳
  17. 是否通过游戏缓解压力
  18. 是否佩戴眼镜

数据集创建

• 创建理由: 理解游戏对学生学术表现和生活习惯的影响，为教育者和政策制定者提供决策依据。
• 数据来源: 通过学生填写的结构化问卷收集，数据经过处理以确保一致性和准确性。
• 数据生产者: 参与调查的学生，数据已匿名化以保护隐私。

偏见、风险和限制

• 注意事项: 数据集可能存在偏见，不一定能代表所有学生群体。使用时应谨慎考虑其局限性。
• 建议: 建议结合其他数据源使用，以确保分析的稳健性。