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Student Exchange Programme Grant Success Data
Education & Learning Analytics
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About
Exam scores and grant statuses for university students who have applied to the Erasmus exchange programme are detailed in this data. It can be used to analyse student performance across various faculties and departments. Furthermore, the data is suitable for building a predictive model to determine if a student with a specific score from a particular faculty is likely to receive a grant, keeping in mind that grant allocation is influenced by faculty-specific quotas.
Columns
- INDEX: A unique index number for each record.
- COUNTRIES: The destination countries students will attend for their Erasmus programme.
- UNIVERSITIES: The specific universities students will attend.
- FACULTIES: The university faculties where the students are currently enrolled.
- DEPARTMENTS: The specific departments where the students are studying.
- EXAM SCORE: The scores students achieved on their Erasmus exam.
- GRANT: A binary indicator showing whether a student received a grant (1 for received, 0 for not received).
Distribution
- Format: CSV
- Size: 37.55 kB
- Structure: The dataset contains 341 rows and 7 columns.
Usage
This data is ideal for a range of analytical tasks. It can be used for performance comparisons between different faculties and departments, analysing average exam scores, and investigating the relationship between faculty quotas and scores. It is also well-suited for analysing the distribution of students across different countries and universities. Additionally, the data can be used to create predictive models to determine grant eligibility based on a student's faculty and exam score.
Coverage
The data pertains to students who applied for the Erasmus programme at a single university. It covers applicants across 13 unique faculties and 56 different departments, with planned travel to 22 countries and 132 unique universities. It should be noted that grant allocation is dependent on faculty-specific quotas, which means a high score in one faculty might not guarantee a grant, while the same score in another faculty could.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: To explore trends in student performance and grant allocation across faculties.
- Data Scientists: To build and train machine learning models that predict grant reception.
- Educational Researchers: To study the factors influencing success in the Erasmus application process.
- University Administrators: To analyse departmental performance and review quota allocations for exchange programmes.
Dataset Name Suggestions
- Erasmus Programme Applicant Scores & Grants
- University Erasmus Grant Allocation Data
- Erasmus Exam Performance and Funding Analysis
- Student Exchange Programme Grant Success Data
Attributes
Original Data Source: Student Exchange Programme Grant Success Data
QUALITY