Student Performance Bangladesh
Description
This dataset has been collected to support research on predicting the academic performance of Secondary School Certificate (SSC) and Higher Secondary Certificate (HSC) students in Bangladesh. It comprises responses from many students across various institutions in the country. The dataset includes a diverse set of features that are believed to influence academic outcomes. These features cover a wide range of domains such as: Demographic Information: Age, gender, parental education, and occupation. Academic History: Previous grades, subject preferences, study time, tutoring, etc. Socioeconomic Factors: Family income, number of siblings, living location (urban/rural). Institutional Factors: Type of school/college (public/private), distance from home, teacher-student ratio, etc. Lifestyle and Behavioral Aspects: Sleep habits, screen time, daily routines, mental health indicators, and parental support. The dataset is labeled with the actual academic performance (grades or GPA) of students in SSC and HSC examinations. The goal is to facilitate the development of predictive models and interpretability studies, with a focus on early intervention and academic counseling. The dataset is anonymized and free from personally identifiable information. It is intended for academic research, education policy analysis, and machine learning experimentation. if you use the dataset, please cite "A. A. Maruf, R. Ara Rumy, R. I. Sony and Z. Aung, "Predictive Analysis of Bangladeshi Students’ Academic Performances Using Ensemble Machine Learning with Explainable AI Techniques," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 1200-1205, doi: 10.1109/ICCIT64611.2024.11021990."