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Bangladesh University Admission Predictor Data
Education & Learning Analytics
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About
This newly developed dataset originates from an Undergraduate Admission Test Survey conducted in Bangladesh, providing valuable material for the research domain. It offers insights into the preparation phase of students applying to higher education institutions, capturing data points related to academic history, socioeconomic conditions, and lifestyle choices. The data was collected through a carefully structured 15-question survey, yielding more than 600 samples from students across 15 public and private universities chosen to represent diverse regions of Bangladesh. This resource is essential for analysing factors that contribute to placement success in Bangladeshi universities.
Columns
The dataset includes 15 fundamental features:
- SSC_GPA: Grade Point Average achieved in the Secondary School Certificate Examination (Range: 2.00 to 5.00).
- HSC_GPA: Grade Point Average achieved in the Higher-Secondary School Certificate Examination (Range: 2.00 to 5.00).
- Family_Economy: Categorisation of the family's economic condition (Possible values include Below Average, Average, Medium, Good).
- Residence: Where the student resided during the preparation phase (Village or Town).
- Family_Education: Educational background of the student’s parents (Uneducated or Educated).
- Political_Involvement: Indication of political activity during preparation (No or Yes).
- Social_Media_Engagement: Time spent utilizing social media during preparation (Categorised in hours: 0-1, 1-3, 3-5, More than 5).
- Residence_with_Family: Whether the student stayed with their parents or not during preparation (No or Yes).
- Duration_of_Study: Time dedicated to studying during the preparation period (Categorised in hours: 2-3, 3-5, 5-7, More than 7).
- School_Location: Location of the school attended during SSC (Village or Town).
- College_Location: Location of the college attended during HSC (Village or Town).
- Bad_Habits: Involvement in habits such as smoking, drinking, or drug addiction (No or Yes).
- Relationship: Involvement in any type of relationship (No or Yes).
- External_Factors: Presence of external challenges such as personal issues, health concerns, or financial difficulties (No or Yes).
- University (Class Attribute): Type of university the student gained admission to (Private University or Public University).
Distribution
The dataset contains over 600 samples, structured with 15 essential features, typically stored in a CSV file format (19.86 kB). The class attribute, which identifies the type of university admission secured, is distributed with 260 samples labeled as Private University and 340 samples labeled as Public University. All features show 100% validity for 600 samples, with minimal mismatched records (3 samples mismatched for HSC_GPA) and no missing values recorded. The mean SSC_GPA is 4.85, and the mean HSC_GPA is 4.79.
Usage
This dataset is suitable for:
- Developing predictive models to determine factors influencing success in gaining admission to public versus private universities.
- Analysing socioeconomic variables, such as family economy and parental education, and their correlation with student preparation habits.
- Researching the impact of social factors (e.g., political involvement, relationships, external challenges) on student performance during the test preparation phase.
- Educational policy formulation aimed at addressing disparities in preparation resources across different regions (Village vs. Town school/college locations).
Coverage
The data covers students sampled from 15 public and private universities within Bangladesh. These universities were deliberately selected to provide a broad and inclusive representation of the student demographic across various regions of the country. Data collection was performed via Google Forms. The expected update frequency for this product is annually.
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
- Educational Policy Makers: To evaluate preparation gaps and understand student needs prior to university entry.
- Machine Learning Engineers: For training classification and predictive models focusing on educational outcomes.
- Sociologists and Researchers: To study the relationship between socioeconomic status, lifestyle habits, and academic success in a developing nation context.
- University Admissions Teams: To gain a deeper understanding of the profile of incoming students.
Dataset Name Suggestions
- Bangladesh University Admission Predictor Data
- Undergraduate Preparation Factors Survey
- Bangladeshi Student Admission Insights
- Higher Education Success Factors (Bangladesh)
Attributes
Original Data Source: Bangladesh University Admission Predictor Data
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