Management Model of Higher Education Using VIKOR Method

Effective management of Higher education models play a key role in sustaining institutional performance, sustaining quality assurance, and promoting continuous improvement. As higher education systems grow more complex and dynamic, implementing effective management strategies becomes essential for long-term success and adaptability complex, the integration of strategic management approaches, quality assessment frameworks, and advanced technologies has become essential. This study examines different management models in higher education, emphasizing their influence on academic success, organizational performance, and overall efficiency. stakeholder engagement. Research Significance: The study of higher education management models and their influence on institutional performance, academic standards, and student achievement is essential. With the rise of globalization, rapid technological advancement, and changing educational demands, implementing effective management strategies is essential for long-term sustainability and growth. Methodology: This study uses a mixed-methods approach to examine higher education management models, incorporating both It is a combination of qualitative and quantitative research approaches supported by a comprehensive literature review is conducted to explore the management structures, best practices, and challenges within higher education institutions. Alternatives Precision: Refers to the extent to which repeated measurements produce consistent results. Accuracy: Precision is measured in terms of accuracy, while recall is measured in terms of positive predictions. The accuracy of a classification model refers to its overall correctness. Recall: Recall and precision are key metrics for evaluating the performance of a classifier in binary and multiclass classification tasks. Specificity: Sensitivity and specificity assess how accurately the true and false sets are predicted. Evolution Parameter (NTQ-BDT): The adoption of the Network-based teaching quality with big data technologies (NTQ-BDT) has facilitated a comprehensive evaluation of online education. AI: This study explores artificial intelligence (AI), fuzzy mathematics, and differential modeling after analyzing existing mathematical models and the "knowledge forgetting curve". (AHP): The study combines this study combines the Analytical Hierarchy Process (AHP) and the Elimination and Choice Expressing Reality (ELECTRE) method to improve patient flow and service quality. Conclusion: In summary, effective management of higher education models is crucial for maintaining institutional performance, achieving academic excellence, and meeting stakeholder expectations. This study emphasizes the need to integrate strategic planning, quality assurance mechanisms, and technological innovation to address emerging challenges in the field of higher education. Result: The results of the study highlight the effectiveness of various higher education management models improving institutional performance, academic standards, and student satisfaction. The results indicate that incorporating strategic planning, technological advancements, and data-driven decision-making enhances operational efficiency, promoting sustainable development and adaptability within higher education institutions.