GOOD ACADEMIC GOVERNANCE USING ARIMA MODELS: CASE STUDY OF THE PREDICTION OF NEW STUDENTS AT THE FACULTY OF ECONOMIC AND SOCIAL SCIENCES IN TANGIER, MOROCCO

Автори

  • Ghafir Adil Doctoral Student, Abdelmalek Essaadi University, Research Team in Management & Dynamics of Organizations, Morocco
  • Bennani Anas Professor, Abdelmalek Essaadi University, Research laboratory Innovative Systems Engineering, Morocco
  • El Hajjaji Soukaina Professor, Abdelmalek Essaadi University, Research Team in Management & Dynamics of Organizations, Morocco
  • Tali Abdelhak Professor, Abdelmalek Essaadi University, Information and Communication Technologies Laboratory, Morocco

ДОИ:

https://doi.org/10.46827/ejefr.v8i8.1932

Клучни зборови:

academic governance, decision-making, forecasting, ARIMA, GARCH

Апстракт

Currently, good governance of Moroccan universities plays a crucial role in guiding their strategies to achieve objectives such as efficient resource allocation and improvement of educational performance. Accurate estimates of future student numbers are essential to actively participate in the effective management of human resources, infrastructure and academic programs. In this context, this work aims to predict the number of students enrolled in the fundamental license of the Faculty of Economic and Social Sciences of Tangier, reporting to the Abdelmalek Essaâdi University in Morocco. The choice of the essential degree is explained by its open access character, which guarantees students wishing to continue their studies in this cycle enrollment without restriction. In addition, this institution was selected because of the particularly high number of new students it registers, surpassing other institutions of the university. This study is based on actual data provided by the establishment, which encourages scientific research to ensure good governance in the coming years. This study was carried out using the ARIMA model.

JEL: I23; C53; H75; O21

 

Article visualizations:

Hit counter

Биографии на авторите

  • Ghafir Adil, Doctoral Student, Abdelmalek Essaadi University, Research Team in Management & Dynamics of Organizations, Morocco

    Adil Ghafir is a PhD student in university governance with a strong interest in artificial intelligence and decision-making. His research focuses on optimizing academic decision-making processes and designing intelligent assistants. He also has knowledge in developing AI agents and multi-agent systems, aiming to enhance efficiency and automation in academic and institutional management.

  • Bennani Anas, Professor, Abdelmalek Essaadi University, Research laboratory Innovative Systems Engineering, Morocco

    Anas Bennani is a professor and researcher at Abdelmalek Essaâdi University, Morocco specializing in data analysis, decision support systems, and public sector digital policies. Passionate about AI, NTIC, and their legal frameworks, he researches semantic web technologies and their applications. He is committed to advancing digital transformation and higher education in Morocco.

  • El Hajjaji Soukaina, Professor, Abdelmalek Essaadi University, Research Team in Management & Dynamics of Organizations, Morocco

    Abdelhak Tali is a professor and researcher at Abdelmalek Essaâdi University, Morocco. He specializes in artificial intelligence, data science, and blockchain technologies. He coordinates the Master’s program in Data Science for the Digital Economy and Finance. His research focuses on machine learning, biometric identification, and intelligent systems, with expertise in deep learning and big data analytics.

  • Tali Abdelhak, Professor, Abdelmalek Essaadi University, Information and Communication Technologies Laboratory, Morocco

    Soukaina El Hajjaji is a professor and researcher at the Faculty of Legal, Economic, and Social Sciences in Tangier, Morocco. She specializes in project and quality management, governance, and leadership, with a focus on university governance, decision-making processes, and institutional performance. Her work aims to improve strategic planning, organizational efficiency, and higher education policies.

Референци

A. Asrirawan, S. U. (2022). Univariate Time Series Modeling Approach for Quarterly Prediction of Indonesia's Economic Growth Post-COVID-19 Vaccination. Jambura Journal of Mathematics, 86–103. doi:10.34312/jjom.v4i1.11717

A. Dela Cruz, M. B. (2020). Higher Education Institution (HEI) enrollment forecasting using data. International Journal of Advanced Trends in Computer Science and Engineering, 2060-2064. Retrieved from https://doi.org/10.30534/ijatcse/2020/179922020

Ahmar, A. S. (2020). Calcul d'erreur de prévision avec erreur quadratique moyenne (MSE) et erreur absolue moyenne en pourcentage (MAPE). Indonesia: Yayasan Ahmar Cendekia Indonesia. Retrieved from https://doi.org/10.35877/454RI.jinav303

Arindrajit Pal, J. P. (2015, 03). Path length prediction in MANET under AODV routing: Comparative analysis of ARIMA and MLP model. Egyptian Informatics Journal, 16(1), 103-111. Retrieved from https://doi.org/10.1016/j.eij.2015.01.001

D. Wulan Sari, R. G. (2016). Parameter estimation of an ARIMA model for river flow forecasting using least squares and goal programming. Jurnal EKSPONENSIAL. Retrieved from http://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/57

George E. P. Box, G. M. (1976). Time Series Analysis: Forecasting and Control. Revised Edition, San Francisco: Holden Day. Retrieved from https://doi.org/10.1111/jtsa.12194

Gerda Claeskens, N. L. (2008). Model Selection and Model Averaging. Cambridge: Press, Cambridge University. Retrieved from https://doi.org/10.1017/CBO9780511790485.003

Gilbert M. Masinading, S. E. (2024, October 24). Forecasting the semestral enrollment of DOrSU curricular programs. Advances and Applications in Statistics, 1579 -1592. Retrieved from https://doi.org/10.17654/0972361724080

Goodfellow, I. B. (2016). Deep Learning. Cambridge, MA: MIT Press.

Ilan, A. (2005). Forecasting University Enrollment: An Historical Case of a College of Business in Northeast United States of America. Journal of College Teaching & Learning, 2(4). doi:10.19030/tlc.v2i4.1803

Lan Luo. (2024, 09 20). Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models. Theoretical and Natural Science, 42, 99-104. Retrieved from https://doi.org/10.54254/2753-8818/42/20240672

Neusser, K. (2016). Time series econometrics. Springer. Retrieved from https://link.springer.com/book/10.1007/978-3-319-32862-1

Pereira, S. H. (2006). Propriétés des estimations GARCH sur petits échantillons et persistance. (Routledge, Ed.) European Journal of Finance, 473-494. Retrieved from https://doi.org/10.1080/13518470500039436

Søren Johansen, M. R. (2012, 11 8). The Selection of ARIMA Models with or Without Regressors. (U. o. Discussion, Ed.) Social Science Research Network, 12-17. Retrieved from http://dx.doi.org/10.2139/ssrn.2175553

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

Преземања

Објавено

2025-04-29

Издание

Том. 8 Бр. 8 (2025)

Секција

Articles