Topological network analysis and its application on revealing dimensions of student satisfaction under the COVID-19 pandemic - Mansooreh Kazemilari; Dalia Štreimikienė (Contemporary Economics, 2024)
Topological network analysis is an advanced tool for revealing customer satisfaction in marketing research and other fields. The university sector plays a vital and competitive role in contributing to a country's development. Students are regarded not only as a source of income but also as a key criterion for attaining the vision and mission of reaching international standards for universities. Student satisfaction is characterized as a comprehensive assessment of the educational experience, formed by comparing initial expectations with the perceived performance following the completion of the educational cycle. The assessment of students' satisfaction is a continuous process that demands ongoing monitoring to maintain high ranks of quality. Because of the disturbance caused by the COVID-19 pandemic, academic institutions adjusted their curricula and delivery methods to align with the emerging norms of online learning. Assessing student satisfaction is critical for determining how effective the online teaching method is. To understand the key factors influencing student satisfaction, the study examines the interconnections among three dimensions (24 items) by using correlation network analysis and Minimal Spanning Tree (MST) methods. Dioid algebra simplifies the MST search into a single step by constructing a converging sequence, eliminating the need for convergence verification, and centrality measures are employed to interpret and present the network results. The findings suggest a more precise conclusion: To enhance student satisfaction, it is imperative to allocate greater focus towards refining teaching methodologies, enhancing the pandemic study program, and improving the course content for online education.
Keywords: I23; C55; I12; D85; 330; topological network analysis; dioid algebra; student satisfaction; online learning