Fuzzy Vikor Application for Learning Management Systems Evaluation in Higher Education

Ayouni, Sarra; Menzli, Leila Jamel; Hajjej, Fahima; Maddeh, Mohamed; Al-Otaibi, Shaha

doi:10.4018/ijicte.2021040102

Cited by 30 publications

(15 citation statements)

References 32 publications

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“…It is essential to define the scope of student engagement in a specific context in order to be able to assess it effectively [ 9 , 17 , 43 , 47 ]. In this article, the dynamic nature of the behavioral, social, and cognitive dimensions of student engagement is captured based on student activities, and actions on a course from LMS reports [ 48 ]. To answer the first research question (Question1: Is it possible to classify the student as Actively Engaged (AE), Passively Engaged (PE) or Not Engaged (NE) based on students’ activities on a course that are recorded in the LMS reports?…”

Section: Methodsmentioning

confidence: 99%

A new ML-based approach to enhance student engagement in online environment

et al. 2021

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The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

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Section: Methodsmentioning

confidence: 99%

A new ML-based approach to enhance student engagement in online environment

et al. 2021

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“…Since then, the fuzzy VIKOR has been used alone or with other methods. The Fuzzy VIKOR approach was employed and efficiently deployed to tackle a broad variety of MCDM issues (Ayouni et al, 2021;Balin et al, 2020;Jing et al, 2018;Meksavang et al, 2019). In this paper, the Fuzzy VIKOR method is modified and applied with other fuzzy MCDM methods to evaluate the QoS factors of web and cloud services.…”

Section: Fuzzy Vikormentioning

confidence: 99%

An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA

al.¹

2022

Int. j. adv. appl. sci.

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Quality of service (QoS) is a very important concept in service-oriented architecture (SOA). While there is a growing body of study on QoS-based service selection based on SOA, there is little research on analyzing QoS factors from the viewpoints of IT workers and researchers. As a result, the purpose of the current study aims to offer an integrated fuzzy VIKOR-TOPSIS-DEMATEL approach framework for evaluating QoS factors of online services from the viewpoint of experts in a fuzzy environment. A numerical assessment of the QoS factors for a case firm in Ghana indicated that the suggested technique is appropriate for the problem. Furthermore, the technique outcomes divided QoS factors into cause-effect variables, ranked QoS factors, and lastly, suggested conflicting QoS factors. The results from the Fuzzy DEMATEL aspect of the proposed approach found integrity, availability, accessibility, compliance, documentation, latency, and adaptability as causal variables. While response time, cost/price, reliability, performance, security, reputation, throughput, best practices, success ability, encryption, portability, storage, and consistency are regarded as influential variables. The Fuzzy TOPSIS aspect of the technique found adaptability, documentation, consistency, transaction, and accessibility are the most ranked QoS factors of online services. The fuzzy VIKOR side of the proposed method discovers integrity, cost, and latency as incommensurable QoS factors. Finally, a sensitivity analysis was carried out, and the results show the model is robust. This study confirms the position of existing knowledge on sensitivity analysis in the QoS literature. In the issue of QoS factor evaluation, this work effectively blended three MCDM techniques. The study's shortcoming stems from its reliance on data from QoS specialists from only one developing nation (i.e. Ghana).

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“…Since existing various fuzzy decision-making methods (Bender and Simonovic 2000;Ansari et al 2016;Rahimi and Najafi 2016;Pandey et al 2019;Wang et al 2019;Bostancı and Erdem 2020;Naeem et al 2020;Yadegaridehkordi et al 2020;Sun et al 2021;Ayouni et al 2021;Shweta 2021) do not consider credibility measures of fuzzy evaluation values in decision-making/evaluation process, the decision information may miss/lack the credibility measure values of fuzzy evaluation values so as to result in the decision distortion or unreasonable decision results due to the importance of the credibility degrees in MADM problems. Then, the proposed MADM method in this study can make the fuzzy evaluation information and decision results more credible and more reasonable in the FCS setting.…”

Section: Comparative Analysis Of the Decision-making Examplementioning

confidence: 99%

“…Furthermore, the review and comparison of many methods have been indicated in (Triantaphyllou and Lin 1996;Abdullah 2013;Kahraman et al 2015). More recently, various fuzzy approaches were proposed and used for decision-making and evaluation problems in the fuzzy environment (Wang et al 2019;Naeem et al 2020;Yadegaridehkordi et al 2020;Sun et al 2021;Ayouni et al 2021;Shweta 2021).…”

Section: Introductionmentioning

confidence: 99%

Weighted aggregation operators of fuzzy credibility numbers and their decision-making approach for slope design schemes

Song

et al. 2021

Comp. Appl. Math.

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In fuzzy decision-making problems, existing fuzzy decision-making methods only indicate fuzzy assessment values but lack the degrees/levels of credibility regarding the fuzzy assessment values in alternatives over attributes due to the vagueness and uncertainty of human cognitions/judgments for complicated decision-making problems. Therefore, the credibility degree of the fuzzy assessment value shows its importance and necessity in the fuzzy decisionmaking problem. To enhance the credibility degrees/levels of fuzzy assessment values, the fuzzy assessment values should be closely related to their credibility measures, which make the assessment information more abundant and more credible. Hence, this study proposes the concept of a fuzzy credibility number (FCN) as a new extension of the fuzzy concept, where both a fuzzy value and a credibility degree are expressed by a pair of fuzzy values. Then, we present operations of FCNs, a score function of FCN, a FCN weighted arithmetic averaging (FCNWAA) operator, and a FCN weighted geometric averaging (FCNWGA) operator. Next, a multi-attribute decision-making (MADM) approach using the FCNWAA or FCNWGA operator is established to deal with MADM problems in the setting of FCNs. Eventually, the established MADM approach is used for a decision-making example on selecting a suitable slope design scheme for an open pit mine in the setting of FCNs to reflect the usability and effectiveness of the established decision-making approach. Compared with the classical fuzzy MADM approach, the established MADM approach makes the assessment information more abundant and more credible, then the sensitivity analysis indicates the importance and efficiency of credibility measures in the ranking order of alternatives, which reflect main advantages of the established MADM approach in the environment of FCNs. However, this study reflects the novelty and new contribution of the proposed information expression and aggregation operations and decision-making method in fuzzy decision theory and methods.

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Fuzzy Vikor Application for Learning Management Systems Evaluation in Higher Education

Cited by 30 publications

References 32 publications

A new ML-based approach to enhance student engagement in online environment

A new ML-based approach to enhance student engagement in online environment

An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA

Weighted aggregation operators of fuzzy credibility numbers and their decision-making approach for slope design schemes

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