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Student Admissions Classification Dataset
Education & Learning Analytics
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About
Academic performance metrics and admission outcomes from the Chilean higher education system facilitate the analysis of factors influencing university acceptance. Key variables include the Aptitude Test for Higher Studies (PAES) scores, secondary education grade averages (NEM), and class ranking classifications. This collection enables the exploration of relationships between standardized testing, high school performance, and successful entry into university programmes, serving as a foundation for educational research and predictive modelling.
Columns
- Unnamed: 0: A unique numerical identifier for each student record in the dataset.
- admit: A binary indicator representing the admission result, where 1 signifies the applicant was admitted and 0 indicates they were not.
- paes: The score achieved on the Aptitude Test for Higher Studies (PAES), ranging from a minimum of 394 to a maximum of 986 in this sample.
- nem: The Average of Secondary Education Grades, recorded on a scale from 1.0 to 7.0, with a mean of approximately 5.91.
- rank: The applicant's class classification rank, where lower values (ranging from 1 to 4) denote superior positions relative to peers.
Distribution
The dataset comprises 1,813 individual records structured across 5 columns. The data is fully valid with no missing or mismatched values (0% missing). The distribution includes 570 admitted applicants and 1,243 non-admitted applicants. PAES scores are distributed with a mean of 493 and a standard deviation of 154, while NEM scores cluster around a mean of 5.91. The data is available in CSV format (AdmisionUes.csv).
Usage
- Predictive Modelling: Training machine learning algorithms to predict university admission probability based on academic history.
- Educational Analysis: Identifying trends and correlations between high school performance (NEM/Rank) and standardised test success (PAES).
- Admissions Strategy: Generating visualisations to understand score distributions and set benchmarks for future intake processes.
- Pedagogical Research: Studying the weight of different academic factors in the selection process.
Coverage
- Geographic Scope: Chile (University of Santiago de Chile context).
- Demographic Scope: University applicants.
- Temporal Scope: Static historical data (Update frequency: Never).
- Data Completeness: 100% valid records for all variables including PAES, NEM, and Rank.
License
CC BY-NC-SA 4.0
Who Can Use It
- Data Science Students: For training classification models and practising exploratory data analysis.
- Educational Researchers: To analyse the fairness and efficacy of admission metrics.
- University Administrators: For auditing historical admission trends.
- Policy Analysts: Evaluating the impact of standardised testing vs. classroom grades.
Dataset Name Suggestions
- Chilean University Admissions Predictors
- PAES and NEM Academic Performance Data
- Higher Education Acceptance Metrics Chile
- Student Admissions Classification Dataset
Attributes
Original Data Source: Student Admissions Classification Dataset
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