A Decision Support System for the Planning of Academic Processes in Higher Education

Radu Melnic, Victor Ababii, Viorica Sudacevschi, Viorel Cărbune and Silvia Munteanu

Intellectus, 2025, issue 2, 174-182

Abstract: The paper proposes the use of Neural Network models for the efficient planning of admission quotas in higher education institutions, with the aim of optimizing financial and material resource allocation and improving the quality of the educational process. Artificial Neural Networks are employed to identify complex patterns and to make accurate predictions regarding the number of admitted students and their academic performance throughout their studies. A functional diagram of a decision support system based on Artificial Neural Networks is presented, modeling both the admission process and academic performance across semesters and over the entire study cycle. Furthermore, the advantages of applying Artificial Neural Networks are highlighted, such as more efficient resource allocation, higher graduate employment rates, and adaptability to the dynamic requirements of the labor market. The study demonstrates that integrating such models into educational planning can lead to greater decision-making efficiency and a better alignment of the educational offer with economic and social demands. The paper also confirms the effectiveness of Artificial Neural Networks in educational planning, emphasizing their capacity to predict the number of admitted students and their academic performance during their studies, thereby enabling resource optimization, risk reduction, and quality improvement in academic training. The proposed decision support system, based on multilayer networks and adaptive algorithms, facilitates budget adjustments and strategic planning at the institutional level, ensuring a closer alignment of the educational offer with labor market requirements and contributing to the increased employability of graduates.

Keywords: decision support system; artificial neural networks; prediction and planning; educational resource optimization; performance prediction; higher education planning; machine learning models; resource allocation; strategic planning. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:awf:journl:y:2025:i:2:p:174-182

DOI: 10.56329/1810-7087.25.2.16

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