{
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    "description": "Description:\nThe Student_performance_data_.csv dataset is a comprehensive collection of data aimed at analyzing the factors influencing student performance across various dimensions. This dataset encapsulates a variety of attributes including demographic information, study habits, participation in extracurricular activities, and academic outcomes for a sample of students. The purpose of this dataset is to facilitate the exploration of correlations between students' backgrounds, behaviors, and academic success, making it an essential tool for educators, policymakers, and researchers interested in educational studies.\n\nAttribute Description:\n- **StudentID**: Unique identifier for each student (e.g., 2737, 1403).\n- **Age**: Age of the student (e.g., 16, 17, 18).\n- **Gender**: Student's gender (0 for female, 1 for male).\n- **Ethnicity**: Categorical representation of the student's ethnicity (e.g., 0, 1, 3; where each number represents a different group).\n- **ParentalEducation**: Level of parental education (1 to 5 scale, from less to more educated).\n- **StudyTimeWeekly**: Weekly study time in hours (e.g., 10.82, 11.51).\n- **Absences**: Number of absences (e.g., 3, 18).\n- **Tutoring**: Indicates if the student receives tutoring (0 for no, 1 for yes).\n- **ParentalSupport**: Level of parental support (0 to 3 scale, from none to high).\n- **Extracurricular**: Participation in extracurricular activities (0 for no, 1 for yes).\n- **Sports**: Participation in sports (0 for no, 1 for yes).\n- **Music**: Participation in music-related activities (0 for no, 1 for yes).\n- **Volunteering**: Participation in volunteering activities (0 for no, 1 for yes).\n- **GPA**: Grade Point Average of the student (e.g., 1.25, 2.85).\n- **GradeClass**: Yearly grade classification (e.g., 2.0, 4.0; on a 1-5 scale).\n\nUse Case:\nThis dataset is geared towards educational researchers and analysts seeking to decipher the intricate web of factors affecting student achievement. By analyzing this dataset, stakeholders can identify critical patterns and predictors of academic success, thereby informing targeted intervention strategies. Educators can tailor their teaching methods based on insights derived, while policymakers can use the dataset to craft informed educational policies. Additionally, it serves as a pivotal tool for studying the impact of socio-economic factors on education, guiding efforts to bridge achievement gaps among diverse student populations.",
    "format": "arff",
    "uploader": "Iwo Godzwon",
    "uploader_id": 39999,
    "visibility": "public",
    "creator": "\"Rabie El Kharoua\"",
    "contributor": "\"None\"",
    "date": "2024-06-30 18:14:21",
    "update_comment": null,
    "last_update": "2024-06-30 18:14:21",
    "licence": "Attribution-ShareAlike (CC BY-SA)",
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