Student Performance Metrics Dataset

The Student Performance Metrics Dataset provides a diverse collection of academic and non-academic attributes aimed at evaluating factors influencing student performance in higher education. It enables researchers to analyse relationships between student demographics, academic achievements, socio-economic factors, and extracurricular activities.

Dataset Attributes:
Department: The academic department the student is enrolled in (e.g., Computer Science, Business, etc.).
Gender: The gender of the student.
HSC: Score obtained in higher secondary education.
SSC: Score obtained in secondary school education.
Income: Monthly family income of their parents.
Hometown: The type of area where the student resides (e.g., urban, rural).
Computer: Proficiency level in computer usage.
Preparation: Time spent on study preparation outside class hours.
Gaming: Time spent on gaming activities daily.
Attendance: Regularity in class participation.
Job: Indicates if the student has a part-time job.
English: Proficiency in English communication skills.
Extra: Participation in extracurricular activities.
Semester: Current semester the student is enrolled in.
Last: Performance in the last semester.
Overall: Cumulative Grade Point Average (CGPA).

Purpose and Use Cases:
The dataset serves as a resource for educational research, enabling trend analysis and the development of predictive models for academic success. Researchers can explore the impact of socioeconomic status, gender, and extracurricular activities on student performance. Potential use cases include building machine learning models to predict performance and analyzing factors that contribute to student success or dropout risks.

Limitations:
This dataset does not cover all potential influences on student performance, such as personal motivation or health. Future studies can enhance this dataset by including additional variables.

Acknowledgments:
This dataset is compiled as an open resource for academic research. Proper citation is appreciated in academic works utilizing this dataset.

{'The Student Performance Metrics Dataset provides a diverse collection of academic and non-academic attributes aimed at evaluating factors influencing student performance in higher education. It enables researchers to analyse relationships between student demographics, academic achievements, socio-economic factors, and extracurricular activities.': '《学生学业表现指标数据集》汇聚了多样化的学术与非学术属性，旨在评估影响高等学府学生学业表现的因素。该数据集为研究者提供了分析学生人口统计学、学术成就、社会经济因素及课外活动之间关系的可能性。', 'Dataset Attributes:': '数据集属性：', 'Department: The academic department the student is enrolled in (e.g., Computer Science, Business, etc.):': '系别：学生所就读的学术系别（例如，计算机科学、商业等）；', 'Gender: The gender of the student.:': '性别：学生的性别；', 'HSC: Score obtained in higher secondary education.:': 'HSC：在高等中学教育中获得的分数；', 'SSC: Score obtained in secondary school education.:': 'SSC：在中学教育中获得的分数；', 'Income: Monthly family income of their parents.:': '收入：学生父母的月收入；', 'Hometown: The type of area where the student resides (e.g., urban, rural).:': '家乡：学生居住地区的类型（例如，城市、乡村）；', 'Computer: Proficiency level in computer usage.:': '计算机：使用计算机的熟练程度；', 'Preparation: Time spent on study preparation outside class hours.:': '准备：课外学习准备所花费的时间；', 'Gaming: Time spent on gaming activities daily.:': '游戏：每天花费在游戏活动上的时间；', 'Attendance: Regularity in class participation.:': '出勤：课堂参与的规律性；', 'Job: Indicates if the student has a part-time job.:': '工作：指示学生是否有兼职工作；', 'English: Proficiency in English communication skills.:': '英语：英语交流技能的熟练程度；', 'Extra: Participation in extracurricular activities.:': '课外：课外活动的参与情况；', 'Semester: Current semester the student is enrolled in.:': '学期：学生目前注册的学期；', 'Last: Performance in the last semester.:': '上学期：上学期表现；', 'Overall: Cumulative Grade Point Average (CGPA).:': '总体：累积平均绩点（CGPA）；', 'Purpose and Use Cases:': '目的与用例：', 'The dataset serves as a resource for educational research, enabling trend analysis and the development of predictive models for academic success. Researchers can explore the impact of socioeconomic status, gender, and extracurricular activities on student performance. Potential use cases include building machine learning models to predict performance and analyzing factors that contribute to student success or dropout risks.': '该数据集作为教育资源，为教育研究提供支持，有助于趋势分析和预测学业成功的模型开发。研究者可以探究社会经济地位、性别和课外活动对学生表现的影响。潜在用例包括构建预测表现的机器学习模型和分析导致学生成功或辍学风险的成因。', 'Limitations:': '局限性：', 'This dataset does not cover all potential influences on student performance, such as personal motivation or health. Future studies can enhance this dataset by including additional variables.': '本数据集并未涵盖影响学生表现的全部潜在因素，如个人动机或健康状况。未来的研究可以通过包括额外的变量来完善此数据集。', 'Acknowledgments:': '致谢：', 'This dataset is compiled as an open resource for academic research. Proper citation is appreciated in academic works utilizing this dataset.': '本数据集作为学术研究开放资源编制。在利用本数据集的学术作品中，恰当的引用将受到欢迎。'}