Published: 8 July 2024| Version 1 | DOI: 10.17632/fs8p4sxd9d.1
Bilal jutt


The dataset comprises novel aspects, specifically in terms of biochemistry student's academic performance prediction at an early stage using academic and demographic features. The dataset may enable other researchers to conduct comparative studies and compare the findings of their local dataset with ours to further extend and understand the depth and breadth of phenomena under research. Researchers can investigate/compare whether demographic or academic features affect students’ performance based on region, country, program, and semester-wise. The datasets offered may provide an opportunity for researchers interested in performing potential experiments to extend their knowledge about the phenomena from different perspectives: • For educational research, the question of whether the student’s performance can be predicted by analyzing the demographic and academic (pre-admission) features. • Students’ performance can be predicted at an early stage for various departments. • Students’ performance can be predicted at the semester level and degree level. • Also, the relationship between students’ performance and demographic and academic (pre-admission) features can be further explored. • These datasets can make an important contribution to the pragmatic policy-related indicators to adjust the admission criteria so far.



Education, Demographics, Academic Assessment, Predictive Modeling