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Student Exam Performance Data
Education & Learning Analytics
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Free
About
This dataset contains the marks secured by students across various subjects, including mathematics, reading, and writing. It is designed to facilitate the analysis of factors influencing student academic performance, the effectiveness of test preparation courses, and the identification of patterns that contribute to test outcomes. This data is invaluable for understanding student achievement in educational settings.
Columns
- gender: Indicates the gender of the student, categorised as male or female.
- race/ethnicity: Details the student's racial or ethnic background, divided into five distinct groups.
- parental level of education: Specifies the highest education level attained by a student's parent, encompassing six different levels such as 'some college' or 'associate's degree'.
- lunch: Describes the type of lunch the student typically receives, either 'standard' or 'free/reduced'.
- test preparation course: Records whether a student completed a test preparation course, noting 'none' or 'completed'.
- math score: The numerical score achieved by the student in their mathematics examination, ranging from 13 to 100.
- reading score: The numerical score achieved by the student in their reading examination, ranging from 27 to 100.
- writing score: The numerical score achieved by the student in their writing examination, ranging from 23 to 100.
Distribution
The dataset is provided as a CSV file named
exams.csv, with a file size of 71.77 kB. It is structured with 8 columns and contains 1000 individual records or rows. Each column has 100% valid data, ensuring no mismatched or missing values across the dataset. For instance, the 'math score' has a mean of 66.4 and a standard deviation of 15.4, while the 'reading score' has a mean of 69 and a standard deviation of 14.7. The 'writing score' has a mean of 67.7 and a standard deviation of 15.6.Usage
This dataset is ideal for:
- Analysing factors contributing to student test outcomes, such as gender, race/ethnicity, parental education, and lunch type.
- Evaluating the impact and effectiveness of test preparation courses on student scores.
- Identifying patterns and interactions within the student performance data.
- Developing strategies aimed at improving student scores in various subjects.
- Conducting classification and data visualization tasks related to educational performance.
Coverage
The dataset's demographic scope includes details on gender (52% male, 48% female), race/ethnicity (with 'group C' being the most common at 32%), parental education levels (e.g., 'some college' at 22%), and lunch types (65% standard, 35% free/reduced). The data represents 1000 unique student records. Specific geographic or time range coverage is not detailed in the provided information. All data points for these demographic attributes are fully available, with no missing entries.
License
CC0: Public Domain
Who Can Use It
- Educational Researchers: To study the determinants of academic success and understand educational disparities.
- Policy Makers: To formulate evidence-based policies and design targeted educational programmes for student improvement.
- Data Scientists and Analysts: For machine learning applications such as predictive modelling of student performance, statistical analysis, and creating insightful data visualisations.
- Educators: To gain insights into how different background factors influence student performance and to identify areas for pedagogical intervention.
Dataset Name Suggestions
- Student Exam Performance Data
- Academic Performance Analysis Dataset
- Student Test Results
- Educational Achievement Data
- Exam Score Predictor Dataset
Attributes
Original Data Source: Student Exam Performance Data
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