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Student Academic Performance Factors
Education & Learning Analytics
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About
An analytical dataset exploring the factors that influence academic performance among students. This resource provides detailed insights into various personal, familial, and educational variables, curated specifically for use in educational research, data analytics projects, and predictive modelling. The data captures key student characteristics, making it highly valuable for visualising trends, exploring correlations, and training machine learning models related to academic outcomes.
Columns
This collection contains 15 fields detailing student characteristics and performance:
- StudentID: A unique identification number assigned to each student record.
- Age: The age of the student, measured in years (ranging approximately from 15 to 18).
- Gender: The self-reported gender of the student (categories include Male, Female, or Other).
- Ethnicity: The ethnic background of the student.
- ParentalEducation: The highest level of education achieved by either parent.
- StudyTimeWeekly: The total number of hours the student reports spending on study each week (ranging up to 20 hours).
- Absences: The total number of school days missed by the student.
- Tutoring: Indicates whether the student receives additional tutoring (Yes/No).
- ParentalSupport: Indicates whether the parents are supportive of the student’s academic efforts (Yes/No).
- Extracurricular: Indicates participation in extracurricular activities (Yes/No).
- Sports: Indicates participation status in sports activities.
- Music: Indicates participation status in music activities.
- Volunteering: Indicates participation status in volunteering activities.
- GPA: The student’s Grade Point Average.
- GradeClass: A classification of the student's overall grade.
Distribution
The dataset is provided in a clean, structured CSV (Comma-Separated Values) file format, utilising UTF-8 encoding. Each row within the file represents a single student record. The data currently holds 2392 valid records across its 15 available columns. The dataset is structured for immediate use in analytical tools.
Usage
This data is perfectly suited for several analytical applications, including:
- Developing machine learning models for student performance prediction.
- Informing educational policy planning and resource allocation.
- Conducting exploratory data analysis and visualisation to uncover relationships between variables.
- Identifying key influencing factors and potential performance gaps among different student groups.
- Classification and regression projects designed for beginner to intermediate-level analysts.
Coverage
The scope of the data covers a diverse range of students, capturing essential personal, familial, and academic variables that affect learning success. The focus is on the attributes influencing academic outcomes, such as study habits and parental involvement. Specific geographic or time frame details are not included within the metadata.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Educational Researchers: To test hypotheses regarding the impact of demographic factors and external support on student grades.
- Data Scientists: For training and evaluating predictive models related to academic success or failure.
- Policy Planners: To understand which societal or familial factors require intervention to improve overall educational attainment.
- Academic Analysts: For visualising trends and creating data stories about educational disparities.
Dataset Name Suggestions
- Student Academic Performance Factors
- Academic Outcome Predictors
- Educational Success Variables
- Student Performance Data
Attributes
Original Data Source: Student Academic Performance Factors
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