Application of topsis and vikor to compare AHP results in blended learning based on student learning style

• المؤلف الرئيسي: Mohamed Nawi, Zulhilmi
• التنسيق: أطروحة
• اللغة: الإنجليزية; الإنجليزية
• منشور في: 2024
• الموضوعات: T Technology (General)
• الوصول للمادة أونلاين: http://eprints.utem.edu.my/id/eprint/28317/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124256

Blended learning, an instructional strategy integrating traditional and online learning, has gained prominence in education. A critical aspect of its success lies in the accurate identification of students' learning styles for effective customization. Previous studies conducted by (Maidin et al. 2023) were using the Analytical Hierarchy Process (AHP) to determine suitable blended learning model, however, the sensitivity and accuracy of AHP was only at 74%. Thus, this report utilized the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods to compare with AHP results in terms of sensitivity and accuracy towards the blended learning model to student learning styles. The research employs TOPSIS and VIKOR as alternative multi-criteria decision-making tools to evaluate the sensitivity and accuracy of outcomes in the context of blended learning and learning style preferences. The study utilizes a dataset comprising learning style indicators and performance metrics from a diverse student population. The proposed methodology aims to provide a comprehensive understanding of the comparative effectiveness of these decision-making tools in assessing the sensitivity and accuracy of the results. Based on the study, TOPSIS & VIKOR agreed with AHP results that the face-to-face driver model is the most preferred option among students, with AHP assigning it a preference score of 31.33%, while TOPSIS gives it the highest score of 1.000 and VIKOR a score of 0.00. Ultimately, this report offers a nuanced perspective on enhancing the effectiveness of blended learning models through the integration of decision-making tools and considerations of individual learning styles.