Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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FORECASTING THE SEMESTRAL ENROLLMENT OF DOrSU CURRICULAR PROGRAMS

Authors

  • Gilbert M. Masinading
  • Saturnino E. Dalagan, Jr.
  • Shamie Kein R. Manib

Keywords:

projection, strategic planning, budget allocation, administrative decision making, growing demand

DOI:

https://doi.org/10.17654/0972361724080

Abstract

Forecasting of student enrollment is crucial to the institution since it affects institutions’ income, the number of faculty needed, facility requirements, and budgets. Hence, semestral enrollment projection is needed as it is an important process for students and educational institutions. This study is conducted to forecast the semestral enrollment of Davao Oriental State University curricular programs. The data sets consist of 68 time series observations from 1st semester of SY 1990-1991 to 2nd semester of 2023-2024. Results showed that there is an upward trend in the series, with the number of enrolled students increasing from the first semester of AY 1990-1991 to the first semester of AY 2015-2016. Despite a decrease in enrollment from AY 2015-2016 to AY 2017-2018, the trend resumes its upward trajectory until AY 2023-2024. Additionally, the series appears to exhibit seasonality, with the number of enrolled students showing a regularly repeating pattern of high enrollment in the first semester of each academic year and a decline in enrollment in the second semester. The forecasted values of semestral enrollment from AY 2024-2025 to AY 2029-2030 follow an upward trend and a seasonal variation for the first and second semesters. These projections  indicate a significant growth in the university’s enrollment over the next following years. This underscores the growing demand for education at the university and signals the necessity for strategic planning to accommodate future student population. The university’s administration can use these insights for informed decision-making regarding resource allocation, budgeting, infrastructure development, and faculty and staff demand to meet the anticipated rise in student enrollment.

Received: August 20, 2024
Revision: October 3, 2024
Accepted: October 10, 2024

References

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A. Dela Cruz, M. Basallo, B. Bere III, J. Aguilar, C. Calvo, J. Arroyo and A. Delima, Higher Education Institution (HEI) enrollment forecasting using data mining technique, International Journal of Advanced Trends in Computer Science and Engineering 9(2) (2020), 2060-2064.

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J. Ward, Forecasting enrollment to achieve institutional goals, College University Journals 82(2) (2007), 41-46.

https://spu.edu/depts/idm/docs/publications/JW_Publication07.pdf.

A. Hayes, Autoregressive Moving Average (ARIMA) Prediction Model, 2024.

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R Development Core Team, R: A language and environment for statistical computing, Retrieved October 20, 2018, from R foundation for statistical computing: 2011.

https://www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing.

Published

19-10-2024

Issue

Section

Articles

How to Cite

FORECASTING THE SEMESTRAL ENROLLMENT OF DOrSU CURRICULAR PROGRAMS. (2024). Advances and Applications in Statistics , 91(12), 1579-1592. https://doi.org/10.17654/0972361724080

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