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Student Success & Dropout Factors
Education & Learning Analytics
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About
This dataset is designed to predict students' dropout and academic success within a higher education institution. It offers a detailed view of students enrolled in various undergraduate degrees, combining demographic data, social-economic factors, and academic performance information. The dataset can be used to analyse potential predictors of student dropout and academic achievement. It includes multiple separate databases with relevant information available at the time of enrollment, such as application mode, marital status, and chosen course. Furthermore, the data allows for estimating overall student performance at the end of each semester by assessing curricular units credited, enrolled, evaluated, and approved, as well as their respective grades. Economic factors like unemployment rate, inflation rate, and GDP from the region are also included, providing insights into how these influence student dropout rates or academic success outcomes. This tool provides valuable insight into what encourages students to remain in school or discontinue their studies across a wide array of disciplines, including agronomy, design, education, nursing, journalism, management, social service, and technologies.
Columns
The dataset contains 35 columns in the file
dataset.csv:- 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 (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 enrollment: The age of the student at the time of enrollment (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 curricular units evaluated by the student 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 grade obtained for curricular units in the first semester (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 by the student in the second semester (Numerical).
- Curricular units 2nd sem (enrolled): The number of curricular units enrolled by the student in the second semester (Numerical).
- Curricular units 2nd sem (evaluations): The number of curricular units evaluated by the student in the second semester (Numerical).
- Curricular units 2nd sem (approved): The number of curricular units approved by the student in the second semester (Numerical).
- Curricular units 2nd sem (grade): The grade obtained for curricular units in the second semester (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 the region (Numerical).
- Inflation rate: The inflation rate in the region (Numerical).
- GDP: Gross Domestic Product in the region (Numerical).
- Target: The student's outcome (Graduate, Dropout, Other) (Categorical).
Distribution
The dataset is provided as a CSV file, named
dataset.csv, and has a size of 470.86 kB. It contains 4424 records across all 35 columns. The 'Target' column indicates the outcomes, with 50% of students graduating, 32% dropping out, and 18% (794 students) falling into another category. It is worth noting that this dataset currently contains information for only one semester per admission intake.Usage
This dataset can be used to understand and predict student dropouts and academic outcomes. Ideal applications and use cases include:
- Predictive Modelling: Develop models to identify student risk factors for dropout, enabling early interventions to improve student retention rates.
- Performance Analysis: Higher education institutions can better understand their students' academic progress and identify areas for improvement at both individual and institutional levels. This supports the creation of targeted courses, activities, or initiatives to enhance academic performance.
- Policy and Intervention Guidance: The data offers valuable insights into factors affecting student success, which can be used to guide the development of effective interventions and student retention policies.
- Investigating Key Factors: Researchers can investigate which specific factors predict student dropout or completion, and how different features interact with each other. For example, exploring associations between demographic characteristics (like gender or age at enrollment) or wider economic conditions (like regional unemployment rates) and student success.
- Addressing Accessibility Gaps: Demographic information within the dataset can inform the creation of specific initiatives aimed at helping particular groups more easily access higher education services or resources that might not typically be available to them, thereby helping to close existing accessibility gaps.
Coverage
This dataset focuses on students enrolled in undergraduate degrees at a higher education institution. It includes diverse demographic data such as gender, age at enrollment, marital status, and nationality. Social-economic factors are represented by parents' qualifications and occupations, scholarship status, debtor status, and tuition fee payment status. Broader economic context is provided through regional unemployment rates, inflation rates, and GDP. Academic performance is covered by details on curricular units (credited, enrolled, evaluated, approved) and grades from the first and second semesters. The data provides a snapshot for one semester per admission intake.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
This dataset is valuable for a range of users:
- Researchers: To investigate predictive factors linked to student dropout or completion, explore feature interactions, and understand the implications of socio-economic conditions like poverty on educational outcomes.
- Academics and Scientists: To familiarise themselves with various data types (categorical, numerical, ordinal, time-series trends, frequency measurements) and to consider potential biases within the data before conducting analysis.
- Higher Education Institutions and Administrators: To formulate strategies that promote successful degree completion among students from diverse backgrounds, identify at-risk students, improve academic performance, and develop accessibility assistance programmes.
- Data Analysts and Machine Learning Engineers: To develop and refine predictive models for student retention and academic success.
Dataset Name Suggestions
- Student Academic Outcomes Data
- Higher Education Retention Predictor
- Student Success & Dropout Factors
- University Performance Analysis
- Undergraduate Student Pathways
Attributes
Original Data Source: Student Success & Dropout Factors
QUALITY