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Student Performance Analysis Dataset
Education & Learning Analytics
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About
This dataset provides insights into student test scores and the various factors that influence them. It offers a valuable resource for understanding how variables such as gender, ethnicity, parental education level, lunch status, and test preparation course completion correlate with academic performance [1]. The dataset is designed to facilitate analysis into these crucial aspects, aiming to uncover patterns and inform strategies related to educational outcomes [1, 2]. It captures the marks secured by students across mathematics, reading, and writing subjects [1, 2].
Columns
- gender: Indicates the sex of the students, categorised as 'Male' or 'Female'. Approximately 52% of the records are female and 48% are male [1, 3].
- race/ethnicity: Specifies the ethnic background of students, divided into five groups: 'Group A', 'B', 'C', 'D', and 'E'. 'Group C' is the most common with 32% of records, followed by 'Group D' with 26% [1, 3].
- parental level of education: Details the highest educational attainment of the students' parents, including 'bachelor's degree', 'some college', 'master's degree', 'associate's degree', and 'high school'. 'Some college' accounts for 23% and 'associate's degree' for 22% of records [1, 3].
- lunch: Describes the student's lunch status before tests, either 'standard' or 'free/reduced'. 65% of students had standard lunch, while 36% had free/reduced lunch [2, 4].
- test preparation course: Records whether a student completed a test preparation course, indicated as 'completed' or 'none'. 64% of students did not complete a course, while 36% did [2, 4].
- math score: Represents the numerical score secured by students in mathematics. Scores range from 0 to 100, with a mean score of 66.1 and a standard deviation of 15.2 [2, 4, 5].
- reading score: Represents the numerical score secured by students in reading. Scores range from 17 to 100, with a mean score of 69.2 and a standard deviation of 14.6 [2, 5].
- writing score: Represents the numerical score secured by students in writing. Scores range from 10 to 100, with a mean score of 68.1 and a standard deviation of 15.2 [2, 6].
Distribution
The dataset is provided in a CSV (Comma Separated Values) file format [7]. It is approximately 72.04 KB in size and consists of 8 distinct columns [2, 3]. The dataset contains 1000 individual records or rows, with all columns reporting 1000 valid entries and no missing values [3-6].
Usage
This dataset is highly suitable for various analytical purposes, including:
- Understanding the impact of socio-economic and preparatory factors on student academic performance [1, 2].
- Identifying correlations between parental background, test preparation, lunch status, gender, and ethnicity with student test scores [1, 2].
- Developing predictive models to forecast student success or identify at-risk students based on demographic and preparatory variables.
- Informing educational policies and interventions aimed at improving student outcomes and promoting equity in education [1].
- Conducting exploratory data analysis and data visualisation to reveal hidden patterns and insights within student study performance [2].
Coverage
The demographic scope of this dataset is detailed through the gender (Male/Female) and race/ethnicity (Groups A-E) attributes of the students [1, 3]. The dataset contains 1000 student records [3]. Specific geographic locations or the exact time range over which the data was collected are not available in the provided sources.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Educational Researchers: For in-depth studies on factors influencing student learning and performance.
- Policy Makers: To guide decisions on resource allocation, curriculum development, and support programmes for students.
- Data Scientists and Analysts: For applying statistical methods, machine learning algorithms, and creating visualisations to extract insights from educational data.
- Academic Institutions: Useful for teaching purposes, student projects, and case studies in data science and education.
Dataset Name Suggestions
- Student Academic Performance Influencers
- Student Test Scores and Contributing Factors
- Educational Outcome Predictors
- Student Performance Analysis Dataset
- Academic Performance Determinants
Attributes
Original Data Source: Student Performance Analysis Dataset
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