Comprehensive Longitudinal Analysis of Academic Performance and Multi-Variate Grade Metrics for M.Sc. Environmental Science & Technology (EST) Program (2024-25)
Description
Academic performance (AP) in higher education is a critical predictor of professional success and scientific literacy. This dataset provides a comprehensive longitudinal record of the academic outcomes for a cohort of sixteen students enrolled in the M.Sc. Environmental Science & Technology (EST) program during the 2024-25 academic year. The data encompasses four consecutive semesters, mapping the progress of students through a specialized curriculum that integrates ecology, pollution control, industrial safety, and waste management. The primary objective of this dataset is to facilitate Educational Data Mining (EDM) to identify patterns in student success and areas requiring pedagogical intervention. Each entry includes unique enrollment identifiers, subject-specific letter grades, Semester Grade Point Average (SGPA), and Cumulative Grade Point Average (CGPA). Specific courses analyzed include Environmental Biology and Restoration, Air Pollution Control, Industrial Toxicology, and Waste Management Technology. The dataset reveals significant variance in performance across technical versus theoretical modules. For instance, in Semester I, course 201350101 (Environmental Biology) showed a high density of "BC" grades, whereas Semester II saw an improvement in top-tier "AA" grades in 201350201 (Remote Sensing). By Semester IV, the cohort demonstrated stabilization in CGPA ranges, reflecting the impact of cumulative learning and dissertation work. Researchers can utilize this data to model non-linear relationships between academic determinants and outcomes using machine learning algorithms like Support Vector Machines (SVM) or Artificial Neural Networks (ANN). This repository serves as a foundational resource for institutional early-warning systems aimed at reducing dropout rates and enhancing the quality of graduate environmental education.
Files
Steps to reproduce
Data Collection: Student results were extracted from the official institutional examination portal for the M.Sc. EST program (A.Y. 2024-25). Standardization: Raw scores were converted into a standard 10-point letter grading system: AA (10), AB (9), BB (8), BC (7), CC (6), CD (5), DD (4), and FF (0). Course Mapping: Subjects were categorized by semester codes (e.g., 201350101 for Environmental Biology) to ensure consistency across the longitudinal study. Statistical Calculation: SGPA was calculated using the weighted average of grade points and credit hours. CGPA was derived by averaging the SGPAs across all completed semesters. Data Visualization: Histogram distributions were generated for each course to visualize grade density. These plots help identify "bottleneck" courses where students consistently underperform. Anonymization: Student names were replaced with unique 14-digit enrollment numbers (e.g., 22401350301001) to maintain privacy while allowing for cross-semester tracking. Validation: Cross-verification of SGPA/CGPA values was performed using standard institutional formulas to ensure 100% mathematical accuracy before dataset finalization.