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Higher Education Predictor
Education & Learning Analytics
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Free
About
This dataset aims to predict student dropout and academic success within higher education. It was created from various disjoint databases of a higher education institution, focusing on students enrolled in diverse undergraduate degrees such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information available at the time of student enrolment, such as academic path, demographics, and social-economic factors, alongside the students' academic performance at the end of their first and second semesters. The data is specifically designed to build classification models for forecasting student outcomes, formulated as a three-category classification task, which notably has a strong imbalance towards one of the classes.
Columns
- Marital status: The marital status of the student (Categorical).
- Application mode: The method of application used by the student (Categorical).
- Application order: The order in which the student applied, ranging from 0 (first choice) to 9 (last choice) (Numerical).
- Course: The specific course taken by the student (Categorical).
- Daytime/evening attendance: Indicates whether the student attends classes during the day or in the evening (Categorical).
- Previous qualification: The qualification obtained by the student prior to enrolling in higher education (Categorical).
- Nacionality: The nationality of the student (Categorical).
- Mother's qualification: The qualification of the student's mother (Categorical).
- Father's qualification: The qualification of the student's father (Categorical).
- Mother's occupation: The occupation of the student's mother (Categorical).
- Father's occupation: The occupation of the student's father (Categorical).
- Displaced: Indicates whether the student is a displaced person (Categorical).
- Educational special needs: Indicates whether the student has any special educational needs (Categorical).
- Debtor: Indicates whether the student is a debtor (Categorical).
- Tuition fees up to date: Indicates whether the student's tuition fees are up to date (Categorical).
- Gender: The gender of the student (Categorical).
- Scholarship holder: Indicates whether the student is a scholarship holder (Categorical).
- Age at enrolment: The age of the student at the time of enrolment (Numerical).
- International: Indicates whether the student is an international student (Categorical).
- Curricular units 1st sem (credited): The number of curricular units credited by the student in the first semester (Numerical).
- Curricular units 1st sem (enrolled): The number of curricular units enrolled by the student in the first semester (Numerical).
- Curricular units 1st sem (evaluations): The number of evaluations for curricular units in the first semester (Numerical).
- Curricular units 1st sem (approved): The number of curricular units approved by the student in the first semester (Numerical).
- Curricular units 1st sem (grade): The average grade in the first semester, between 0 and 20 (Numerical).
- Curricular units 1st sem (without evaluations): The number of curricular units without evaluations in the first semester (Numerical).
- Curricular units 2nd sem (credited): The number of curricular units credited in the second semester (Numerical).
- Curricular units 2nd sem (enrolled): The number of curricular units enrolled in the second semester (Numerical).
- Curricular units 2nd sem (evaluations): The number of evaluations for curricular units in the second semester (Numerical).
- Curricular units 2nd sem (approved): The number of curricular units approved in the second semester (Numerical).
- Curricular units 2nd sem (grade): The average grade in the second semester, between 0 and 20 (Numerical).
- Curricular units 2nd sem (without evaluations): The number of curricular units without evaluations in the second semester (Numerical).
- Unemployment rate: The unemployment rate in percentage (Numerical).
- Inflation rate: The inflation rate in percentage (Numerical).
- GDP: Gross Domestic Product (Numerical).
- Target: The classification outcome, which can be dropout, enrolled, or graduate at the end of the normal duration of the course (Categorical).
Distribution
The dataset is typically provided as a CSV data file. The sample file,
dataset.csv, is 470.86 kB in size. It contains 35 columns and consists of 4424 valid records.Usage
This dataset is ideal for:
- Developing predictive models for student dropout.
- Creating models to forecast student academic success.
- Building differentiated models to understand and predict various student outcomes.
- Applying and testing classification algorithms for student performance analysis.
- Enabling educational institutions to identify at-risk students and implement targeted interventions.
Coverage
The dataset covers students from a higher education institution, encompassing various undergraduate degree programmes. It includes demographic information such as marital status, nationality (e.g., Portuguese, German, Angolan, Brazilian), age, and gender, as well as socio-economic factors like parental qualifications and occupations, debtor status, and scholarship holding. Academic performance is covered for the first and second semesters. The data reflects information known at the point of student enrolment and subsequent academic performance.
License
CC0: Public Domain
Who Can Use It
- Researchers and Academics: To advance studies in educational analytics, student retention, and predictive modelling.
- Educational Institutions: For strategic planning, student support services, and early warning systems to improve student outcomes.
- Data Scientists and Machine Learning Engineers: To develop, refine, and validate classification models, particularly for imbalanced datasets.
- Policy Makers: To inform the development of educational policies and resource allocation aimed at enhancing student success and reducing dropout rates.
Dataset Name Suggestions
- Student Academic Outcomes
- University Student Retention
- Higher Education Predictor
- Student Success Forecaster
- Academic Performance Indicators
Attributes
Original Data Source: Higher Education Predictor
QUALITY