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PRIORITISING TEACHING MODALITIES ON STUDENT PERFORMANCE BY
ROUGHNESS SIMILARITY MEASURE OF ROUGH NEUTROSOPHIC SET
NAME : NURAINASOFEA BINTI MOHAMAD ZAWAWI (K242/57)
SUPERVISOR’S NAME : DR SURIANA BINTI ALIAS
FACULTY OF COMPUTER AND MATHEMATICAL SCIENCES, UITM CAWANGAN KELANTAN KAMPUS MACHANG
ABSTRACT IMPLEMENTATION
Roughness approximation set A
PHASE 1 : FORMULATE THE PROPOSED ROUGHNESS SIMILARITY MEASURE
Prioritising teaching modalities can be a challenging task
considering a few factors such as enviromental factros and
individual differences. Traditional methods of evaluating
teaching modalities often struggle to deal with the uncertainty The new similarity measure rough neutrosophic sets between any two RNSs A and B
and complexity of these data very well, which may lead to satisfy the following properties :
inaccurate results. This study develops an approach using a Roughness approximation set B
roughness similarity measure in a rough neutrosophic set
framework to over- come this problem. In this research, the new
proposed similarity measure which is the roughness similarity
measure was created. Hence, this research aims to apply the
proposed roughness similarity measure for prioritising teaching The result of calculation
modalities on student performance. The roughness Proof :
approximation for a rough neutrosophic set was used in the (P1) : 0 ≤ S RNS (A,B) ≤ 1
definition of the similarity measures. In the next phase, the
analytical framework for determining the most effective teaching
modalities on student performance is presented. Then, to Numerical example set A Numerical example set B The truth (T) roughness
compare the similarity results, the roughness approximation for measure for set A indicates
for X is calculated as follows :
1
a rough neu- trosophic set will be utilized. The validation of
results has been verified. The similarity properties are used as a
validation procedure to determine the most effective teaching RESULT & DISCUSSION
modalities on students performance such as face-to-face
teaching, pure online teaching, blended teaching, and flipped Roughness measure for set A Roughness measure for set B
classroom teaching. Lastly, a strong relationship between the The final result for this project :
provided information or vice versa is defined if either value of
the similarity measure is close to one.
Then, by using the proposed roughness similarity measure set A and set B is calculated as follows :
PROBLEM STATEMENT
(P2) : S RNS (A,B)=1 ⇔ A=B which states that when A=B, then obviously S RNS (A,B)=1
In recent years, evaluating and prioritising teaching modalities has
become increasingly important due to the increasing variety of As the term roughly suggests, roughness approximation is an
teaching modalities. While this diversity offers opportunities, it important criterion for a rough set measure because it determines
also presents challenges in optimising student performance. This implies that A = B the granularity level of the information provided. It focuses on the
Educational institutions struggle to align teaching modalities with relationship between a lower and upper approximation in a rough
(P3) : S RNS (A,B)= S RNS (B,A)
students' needs, as differences in learning styles, cognitive abilities, boundary set.
and engagement levels can lead to varying responses to different
teaching modalities. This complexity makes it difficult to identify The comparison result of the existing with the same case study
the most effective teaching modalities, and traditional evaluation Therefore,
methods often fall short in handling the uncertainty and ambiguity
in educational data.. To address this, the study proposes using the
rough neutrosophic set (RNS), a mathematical tool designed to The calculation findings showed that A1 which represents face-to-
manage uncertainty in decision-making. The goal is to prioritise face teaching. is the most effective teaching modalities on student
the most effective teaching modalities using a technique that (P4) : S RNS (A,C)≤ S RNS (A,B) and S RNS (A,C)≤ S RNS (B.C) if A⊆B⊆C , when C ∈ RNS. If A⊆B⊆C performance with a similarity measure of 0.9714.
accounts for imprecise and uncertain data, providing a more for A,B,C ∈ RNS, then :
S RNS (A,C)≤ S RNS (A,B) and S RNS (A,C)≤ S RNS (B.C)
accurate method for decision-makers to enhance student
performance. Let A⊆B⊆C , which implies that : Then, we obtain the following relation:
a)
CONCLUSION
b)
OBJECTIVES This project introduced a roughness and similarity measure for
for every x
c)
i
rough neutrosophic sets (RNS) to prioritize teaching modalities. It
Combining a), b), and c), we obtain:
defined key evaluation criteria and used lower and upper
1. To propose a roughness similarity measure for rough approximations to assess roughness and similarity. The results
neutrosophic set. and showed that the proposed method effectively ranked teaching
2. To apply the proposed roughness similarity measure for modalities and handled uncertainty well, thus making it a more
prioritising teaching modalities on students performance. Implies that S RNS (A,C)≤ S RNS (A,B) and S RNS (A,C)≤ S RNS (B.C) robust option than traditional multi-criteria decision-making
models in educational contexts.
PHASE 2 : THE DETERMINATION OF ROUGHNESS SIMILARITY MEASURE FOR
PRIORITISING TEACHING MODALITIES ON STUDENTS PERFORMANCE
METHODOLOGY Linguistic value for Rough Neutrosophic Set
RECOMMENDATIONS
Future studies could explore into adaptive or dynamic weighting
methods for criteria based on real-time analytics of student
Relation between the alternative and criteria (set A) performance. Moreover, exploring other mathematical models like
interval-valued neutrosophic sets can enhance accuracy. Finally,
validating the model in different educational settings will ensure its
wider applicability and reliability in diverse academic environments.
REFERENCES
Relation between the expert and criteria (set B)
Chopra, N., Sindwani, R., and Goel, M. (2022). Prioritising teaching
modalities by extending topsis to single-valued neutrosophic
environment. International Journal of System Assurance Engineering
and Management, 1–12.
́
́
Rogulj, K., Kilic Pamukovic , J., and Ivic , M. (2021). Hybrid mcdm
́
based on vikor and cross entropy under rough neutrosophic set
theory. Mathematics, 9(12), 1334.
