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All Journal Tsaqofah: Jurnal Penelitian Guru Indonesia Asian Journal of Early Childhood and Elementary Education International Journal of Education, Culture, and Society African Multidisciplinary Journal of Sciences and Artificial Intelligence Open Access DRIVERset
Aman Gupta
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Education Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences Other

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Enrollment, Graduation, and Dropout Trends in Nepalese Higher Education: A Mathematical and Statistical Analysis Aman Gupta; Suresh Kumar Sahani; Aniket Sahani
International Journal of Education, Culture, and Society Vol 4 No 3 (2026): International Journal of Education, Culture, and Society
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijecs.v4i3.12068

Abstract

Nepalese higher education has experienced substantial enrollment expansion, yet the extent to which this growth has translated into graduate output and timely completion remains unclear. This study examines trends in student enrollment, graduate output, and non-completion in Nepalese higher education using a quantitative secondary-data design. The principal national series was drawn from University Grants Commission higher-education management information reports and supplemented by evidence from the Ministry of Finance Economic Survey, National Statistics Office, UNESCO Institute for Statistics, World Bank, Asian Development Bank, and recent peer-reviewed Nepalese studies. Data were analyzed using statistical trend analysis, percentage-change analysis, moving comparisons, ratio analysis, correlation, and a simple least-squares trend model. Between 2016/17 and 2023/24, higher-education enrollment increased from 371,184 to 633,053 students, representing approximately 70.55% growth, while female enrollment rose from 195,133 to 358,140, increasing the female share from 52.57% to 56.57%. Graduate output was substantially more volatile, declining from 90,922 in 2016/17 to 37,619 in 2020/21 before recovering to 97,564 in 2023/24. Because annual national totals do not follow the same students from entry to completion, the study distinguishes the descriptive graduate-to-enrollment ratio from a true cohort graduation rate. The findings indicate that expanded access has not translated proportionately into timely completion, with persistence shaped by the COVID-19 shock, academic calendar delays, uneven institutional quality, migration for study and employment, financial pressures, weak academic integration, and limited labor-market alignment. The study concludes that the central policy challenge has shifted from expanding enrollment alone to converting participation into timely, successful, and relevant graduation. It contributes by providing an integrated national-level assessment of enrollment and completion dynamics and highlights the need for student-level longitudinal data systems, early-warning retention mechanisms, stronger academic advising, flexible financing, curriculum reform, and differentiated support for vulnerable programs and campuses.
Mathematical Analysis of Government Expenditure, Enrollment, and Educational Outcomes Aman Gupta; Suresh Kumar Sahani; Aniket Sahani
TSAQOFAH Vol 6 No 6 (2026): TSAQOFAH: Jurnal Penelitian Guru Indonesia
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/tsaqofah.v6i6.12122

Abstract

The rapid expansion of higher education in Nepal has increased pressure on public financing, institutional capacity, and the effective conversion of educational expenditure into measurable outcomes. This study examines higher-education financing in Nepal through a quantitative framework linking public expenditure, student enrollment, institutional capacity, and educational outcomes. Using secondary data from the University Grants Commission (UGC), Government of Nepal economic surveys, UNESCO Institute for Statistics (UIS), and World Bank analytical documents, the study employed descriptive analysis, growth-rate analysis, ratio measures, elasticity measures, and association analysis. UGC data show that higher-education enrollment increased from 371,184 students in 2016/17 to 633,053 in 2023/24, while public financing allocated to universities and boards increased from NPR 9.30 billion in 2017/18 to NPR 17.457 billion in 2023/24. Graduate output increased from 90,922 in 2016/17 to 97,564 in 2023/24, although a pronounced temporary decline occurred during the COVID-19 period. The findings indicate that although higher-education financing increased substantially in nominal terms, additional expenditure was not proportionately associated with improvements in educational outcomes. Enrollment expansion, institutional concentration, teacher availability, program composition, delayed completion, and expenditure utilization jointly shaped the observed patterns. Because national financial and educational series are not fully synchronized across fiscal and academic years, the identified associations are interpreted descriptively rather than causally. The study concludes that financing reform should move beyond input-oriented allocation toward transparent, formula-based, and performance-sensitive mechanisms. The findings contribute to understanding the relationship between financing expansion and higher-education outcomes in Nepal and highlight the need for stronger investment in research, quality assurance, student completion, digital infrastructure, equitable regional access, and harmonized national data systems to support future econometric analysis.
Higher Education Financing in Nepal: Mathematical Analysis of Government Expenditure, Enrollment, and Educational Outcomes Aman Gupta; Suresh Kumar Sahani; Aniket Sahani
Asian Journal of Early Childhood and Elementary Education Vol 4 No 5 (2026): Asian Journal of Early Childhood and Elementary Education
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajecee.v4i5.12065

Abstract

Rising enrollment and public expenditure in Nepalese higher education have intensified the need to assess whether financing expansion corresponds with institutional capacity and educational outcomes. This study examines the relationships among public expenditure, student enrollment, institutional capacity, and educational outcomes in Nepal. It employs a quantitative framework based on secondary data from the University Grants Commission, Government of Nepal economic surveys, UNESCO Institute for Statistics, and World Bank analytical documents. The data were analysed using descriptive statistics, growth rates, ratios, elasticity measures, and measures of association. University Grants Commission data show that higher education enrollment increased from 371,184 students in 2016/17 to 633,053 in 2023/24, while public financing for universities and boards rose from NPR 9.30 billion in 2017/18 to NPR 17.457 billion in 2023/24. Graduate output increased more modestly, from 90,922 to 97,564, with a pronounced temporary decline during the COVID-19 period. Although financing grew substantially in nominal terms, additional expenditure was not proportionately associated with improved educational outcomes. The observed patterns were also shaped by enrollment expansion, institutional concentration, teacher availability, programme composition, delayed completion, and expenditure utilisation. These findings indicate that financing reform should move beyond input-based allocation toward transparent, formula-based, and performance-sensitive mechanisms that support research, quality assurance, programme completion, digital infrastructure, and equitable regional access. Because the national datasets are not fully synchronised across fiscal and academic years, the identified relationships are interpreted as descriptive associations rather than causal effects. The study contributes an integrated quantitative assessment of Nepalese higher education financing and identifies data harmonisation as a prerequisite for more rigorous econometric analysis and evidence-based resource allocation.
Mathematical Modeling and Deep Learning for Early Prediction of Fluid Hazards in Mountain Communities Aman Gupta; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 3 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i3.12121

Abstract

Mountain communities are increasingly exposed to rapidly evolving fluid hazards, including flash floods, rainfall-triggered debris flows, glacial lake outburst floods (GLOFs), and flood–landslide cascades. Their short response times, complex terrain, sparse observational networks, and changing hydro-climatic conditions limit the generalizability of conventional threshold-based warning systems. This study develops a hybrid research framework that integrates physically interpretable mathematical modeling with deep learning for the early prediction of fluid hazards in mountain environments, with particular relevance to Nepal. The proposed framework integrates rainfall, snow and glacier indicators, soil moisture, terrain attributes, river stage and discharge, surface deformation, and historical hazard observations. A rainfall–runoff and threshold-based component provides physical constraints, while LSTM and CNN-LSTM models capture nonlinear temporal and spatial relationships. A probabilistic decision layer then translates model outputs into actionable warning levels while explicitly representing predictive uncertainty. Model performance is evaluated using precision, recall, F1-score, false-alarm ratio, probability of detection, critical success index, lead time, and calibration. Evidence considered in the study indicates that LSTM models can outperform conceptual rainfall–runoff models in some mountainous catchments in Nepal, while community-based warning systems can substantially extend warning lead time when forecasts are effectively integrated with local response mechanisms. The study further identifies sensor limitations, domain shift, rare-event imbalance, communication failures, model interpretability, and institutional coordination as major implementation challenges. The proposed framework contributes an explainable and community-oriented architecture for data-scarce mountain regions by combining physical process representation, machine-learning-based hazard probability estimation, uncertainty communication, and locally meaningful warning dissemination. It provides a basis for developing next-generation early-warning systems in which mathematical models constrain prediction, machine learning updates hazard probabilities, and communities receive timely and actionable warnings.
Mathematical Modeling and Deep Learning for Early Prediction of Fluid Hazards in Mountain Communities Aman Gupta; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 3 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i3.12121

Abstract

Mountain communities are increasingly exposed to rapidly evolving fluid hazards, including flash floods, rainfall-triggered debris flows, glacial lake outburst floods (GLOFs), and flood–landslide cascades. Their short response times, complex terrain, sparse observational networks, and changing hydro-climatic conditions limit the generalizability of conventional threshold-based warning systems. This study develops a hybrid research framework that integrates physically interpretable mathematical modeling with deep learning for the early prediction of fluid hazards in mountain environments, with particular relevance to Nepal. The proposed framework integrates rainfall, snow and glacier indicators, soil moisture, terrain attributes, river stage and discharge, surface deformation, and historical hazard observations. A rainfall–runoff and threshold-based component provides physical constraints, while LSTM and CNN-LSTM models capture nonlinear temporal and spatial relationships. A probabilistic decision layer then translates model outputs into actionable warning levels while explicitly representing predictive uncertainty. Model performance is evaluated using precision, recall, F1-score, false-alarm ratio, probability of detection, critical success index, lead time, and calibration. Evidence considered in the study indicates that LSTM models can outperform conceptual rainfall–runoff models in some mountainous catchments in Nepal, while community-based warning systems can substantially extend warning lead time when forecasts are effectively integrated with local response mechanisms. The study further identifies sensor limitations, domain shift, rare-event imbalance, communication failures, model interpretability, and institutional coordination as major implementation challenges. The proposed framework contributes an explainable and community-oriented architecture for data-scarce mountain regions by combining physical process representation, machine-learning-based hazard probability estimation, uncertainty communication, and locally meaningful warning dissemination. It provides a basis for developing next-generation early-warning systems in which mathematical models constrain prediction, machine learning updates hazard probabilities, and communities receive timely and actionable warnings.
Co-Authors Aniket Sahani Suresh Kumar Sahani