Analyzing online academic interactions through fuzzy and neutrosophic relational maps
DOI:
https://doi.org/10.55573/JISED.118760Keywords:
Online Distance Learning, COVID-19 pandemic, Fuzzy Relational Maps, Neutrosophic Relational MapsAbstract
The transition to Online Distance Learning (ODL) during the COVID-19 pandemic has changed lecturer–student interactions and created challenges in maintaining students’ academic performance. This study applies Fuzzy Relational Maps (FRM) and Neutrosophic Relational Maps (NRM) to analyse the causal relationships between lecturers’ teaching behaviours and students’ academic performance in an ODL environment. Expert opinions from a lecturer and a student at Universiti Teknologi MARA (UiTM) Cawangan Kelantan are used to construct relational matrices and directed graphs representing teaching behaviours and academic performance. Iterative matrix operations were performed to identify fixed points that represent stable interaction patterns. The findings show that FRM provides clearer representations of causal relationships, while NRM is more effective in representing relationships involving uncertainty and indeterminacy. The study demonstrates that FRM and NRM can be useful tools for modelling lecturer–student interactions and supporting educational decision-making. The findings also provide a basis for future research on academic performance under uncertain learning conditions.
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