| ID | Name |
|---|---|
| 21120345 | Nguyễn Văn Trí |
| 21120441 | Dương Huỳnh Anh Duy |
| 21120450 | Trương Thế Hiển |
The Student Performance Factors dataset provides a comprehensive exploration of various factors that influence student performance in exams. It includes data on study habits, attendance, parental involvement, and other contributing aspects. This dataset is ideal for analyzing patterns, building predictive models, and gaining insights into the factors affecting academic success.
- Number of Records: 6,607
- Number of Features: 20
- File Format: CSV
| Attribute | Description |
|---|---|
Hours_Studied |
Number of hours spent studying per week. |
Attendance |
Percentage of classes attended. |
Parental_Involvement |
Level of parental involvement in the student's education (Low, Medium, High). |
Access_to_Resources |
Availability of educational resources (Low, Medium, High). |
Extracurricular_Activities |
Participation in extracurricular activities (Yes, No). |
Sleep_Hours |
Average number of hours of sleep per night. |
Previous_Scores |
Scores from previous exams. |
Motivation_Level |
Student's level of motivation (Low, Medium, High). |
Internet_Access |
Availability of internet access (Yes, No). |
Tutoring_Sessions |
Number of tutoring sessions attended per month. |
Family_Income |
Family income level (Low, Medium, High). |
Teacher_Quality |
Quality of the teachers (Low, Medium, High). |
School_Type |
Type of school attended (Public, Private). |
Peer_Influence |
Influence of peers on academic performance (Positive, Neutral, Negative). |
Physical_Activity |
Average number of hours of physical activity per week. |
Learning_Disabilities |
Presence of learning disabilities (Yes, No). |
Parental_Education_Level |
Highest education level of parents (High School, College, Postgraduate). |
Distance_from_Home |
Distance from home to school (Near, Moderate, Far). |
Gender |
Gender of the student (Male, Female). |
Exam_Score |
Final exam score. |
This dataset can be used for:
- Predictive Modeling: Building models to predict exam scores based on contributing factors.
- Correlational Analysis: Investigating relationships between variables like study habits, attendance, and performance.
- Educational Insights: Gaining insights into how factors such as parental involvement and teacher quality affect academic outcomes.
- Policy Recommendations: Identifying key areas for intervention to improve student performance.
This dataset aims to aid researchers, educators, and data enthusiasts in understanding and analyzing factors that contribute to academic success. Please ensure proper attribution if used for research or publication.
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