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The concepts of sustainability and reusability have great importance in engineering education. In this context, metadata provides reusability and the effective use of Learning Objects (LOs). In addition, searching the huge LO Repository with metadata requires too much time. If the selection criteria do not exactly match the metadata values, it is n...
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Context 1
... the Learning Object with the highest basic value (E) is selected. As shown in Table 4, the highest value means that the selected criterion is defined as more important than the other criterion. The results of the comparison (for each factors pair) are described in terms of the numerical values from 1 (equal value) to 9 (extremely different), where a higher number indicates that the chosen criterion is considered to be more important to a greater degree than the other criterion being compared [32]. ...
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Sustainability and reusability of learning objects are important. In addition, reusability and effective use of learning object are
provided by metadata. There are massive multimedia materials in learning object repository (LOR). Therefore, selection
difficulty of appropriate learning object (LO) issue is emerged. In this context, effective and rel...
Citations
... MCDM has been used in numerous studies within different contexts as well as for different purposes in studies on e-learning. This method is preferred for a variety of purposes, such as: structuring e-learning in higher education (Kadoić et al. 2017); evaluating e-learning platforms (Alice et al. 2012;2011;Gong et al. 2021;Jain et al. 2016); evaluating e-learning systems and websites (Garg et al. 2019;Khan et al. 2019;Munkhtsetseg et al. 2014); selecting the technologically and pedagogically most appropriate e-learning platform (Colace et al. 2006); and evaluating e-learning contents and learning objects (Sharma et al. 2011;Yigit et al. 2014). ...
Distance education (DE) has a unique potential to ensure continuity of education in extraordinary circumstances such as pandemics and earthquakes, in addition to its advantages such as flexibility in terms of time and space and self-paced learning options. Institutional support provided for instructors engaged in DE programmes is one of the factors which directly affect the efficiency of DE and the quality of the education provided. This study aimed to determine the criteria of institutional support offered to DE instructors through document analysis, and to reveal the importance weights of those criteria by using the method of multi-criteria decision-making (MCDM). The author examined the standards published by a number of accreditation institutions and international educational organisations for DE, and determined the importance weights of those criteria by applying an analytic hierarchy process (AHP). She determined seven support criteria, namely (in order of importance): (1) technical training and orientation; (2) teaching material and resource support; (3) technical support; (4) professional development support; (5) support during the course; (6) evaluation and report submission support; and (7) career development, incentives and financial support. In presenting her study, the author provides an effective evaluation framework for DE support systems which includes a comprehensive set of indicators based on a literature review and feedback from experts.
... Different approaches have been used to address the selection problem, greatly expanding state-of-the-art. For example, Amiri [55] used the analytic hierarchy process (AHP) approach to determine the order of importance while choosing an ERP system, while Yigit et al. [56] used AHP to create an interactive model that aided in choosing software for Web-based learning objects. Furthermore, Göztepe [57] used the Analytic Network Process (ANP) method to evaluate and choose the optimal operating system considering organizational aspects and strategic performance metrics. ...
Correlation is an essential statistical concept for analyzing two dissimilar variables’ relationships. Although the correlation coefficient is a well-known indicator, it has not been applied to interval-valued Pythagorean fuzzy soft sets (IVPFSS) data. IVPFSS is a generalized form of interval-valued intuitionistic fuzzy soft sets and a refined extension of Pythagorean fuzzy soft sets. In this study, we propose the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFSS and examine their necessary properties. Based on the proposed correlation measures, we develop a prioritization technique for order preference by similarity to the ideal solution (TOPSIS). We use the Extract, Transform, and Load (ETL) software selection as an example to demonstrate the application of these measures and construct a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The method investigates the challenge of optimizing ETL software selection for business intelligence (BI). This study offers to illuminate the significance of using correlation measures to make decisions in uncertain and complex settings. The multi-attribute decision-making (MADM) approach is a powerful instrument with many applications. This expansion is predicted to conclude in a more reliable decision-making structure. Using a sensitivity analysis, we contributed empirical studies to determine the most significant decision processes. The proposed algorithm’s productivity is more consistent than prevalent models in controlling the adequate conformations of the anticipated study. Therefore, this research is expected to contribute significantly to statistics and decision-making.
... Amiri (2010) used the AHP approach to determine the order of importance while choosing an ERP system. Similarly, Yigit et al. (2014) used AHP to create an interactive model that aided in choosing software for Web-based learning objects. Additionally, Göztepe (2012) used the ANP method to evaluate and choose the optimal operating system in consideration of organizational aspects and strategic performance metrics. ...
The 2-tuple linguistic Fermatean fuzzy set is an effective tool that combines the advantages of the reliable 2-tuple linguistic model with Fermatean fuzzy set. We aim to develop novel decision-making techniques based on 2TLFFS that can handle the situations in which linguistic labels are assigned to given data. The main objective of this study is to investigate ELECTRE II method for group decision-making in 2-tuple linguistic Fermatean fuzzy context and explain its implementation. The first phase employs suitable 2TLFF aggregation operator to assemble the expert's 2TLFF judgments on each alternative and set of criteria. The method then introduces three different sets (2TLFF concordance, 2TLFF indifferent and 2TLFF discordance sets) by pairwise comparison of alternatives. After that, the strong and weak outranking relations are developed through the comparison of concordance and discordance indices with threshold values (three concordance and two discordance levels). The strong and weak outranking graphs visually represent outranking relations that are ultimately investigated through a systematic iterative process that results in the alternatives' forward, reverse, and average rankings. A flowchart is developed to comprehend the algorithm of 2TLFF-ELECTRE II conveniently. A numerical example for the selection of optimal Extract, Transform and Load software for business intelligence describes the proposed decision-making technique. A thorough comparison with 2TLFF-CODAS and existing aggregation operators is carried out to illustrate the validity and supremacy of the proposed technique.
... The AHP procedure may resolve simple or complex problems consisting of conflicting criteria with different levels of hierarchy and structural complexity. The application of AHP may be found in various types of research and in various applications [15,17,[56][57][58][59]. ...
Mobile Learning (M-Learning), driven by technological digital advancement, is one of the essential formats of online learning, providing flexibility to learners. Cloud-based mobile learning (CBML) provides value additions by providing an economic alternative to E-learning. Revolutionary changes in smartphone design and features have enhanced the user experience, thus encouraging mobile learning. During the COVID-19 pandemic, E-Learning and M-Learning allowed continuing education to occur. These methods continue to offer more opportunities to learners than constrained face-to-face classroom learning. There are many main critical success factors (CSFs) and subfactors that play an influential role in sustainable M-Learning success. The current study focuses on the assessment and ranking of various main factors and subfactors of CBML. Analytic hierarchy process-group decision-making (AHP-GDM)- and fuzzy analytic hierarchy process (FAHP)-based methodologies were used to evaluate and model the main factors and subfactors of CBML in crisp and fuzzy environments. Higher education institutes must strive to address these main factors and subfactors if they are to fulfill their vision and mission in the teaching–learning system while adopting sustainable M-Learning.
... Thus, a component's usability should be interpreted as to its ability to be used by the application developer and designer when building a new software product. According to [27], the sub-characteristics of 'Usability' are 'Learnability,' 'Operability,' 'Understandability,' and 'Complexity.' ...
Any organization that intends to use component-based software development, like outsourcing software, must first evaluate existing components against system requirements to find the best fit among many alternatives. As a result, there should be a mechanism to help with decision-making. Our proposed methodology tries to select the best alternative among available components, using the best decision-making approach. As an integrated method for order preference, the methodology in this paper uses two well-known criterion decision-making procedures, namely Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). By analyzing and selecting the optimal solution among a variety of Out Sourcing (OS) modules, the new model design makes the decision-making process easier. We evaluated two software attributes and predicted which was more effective. In this case, the advantage of utilizing AHP is that it allows the developer to evaluate the structure of the OS selection problem and calculate weights for the chosen criteria. After that, the SAW technique is used to calculate the alternatives ratings for OS components. The integration strategy used in our model and the resulting preference indication, which is produced as an explicit numeric value.
... Thus, a component's usability should be interpreted as to its ability to be used by the application developer and designer when building a new software product. According to [27], the sub-characteristics of 'Usability' are 'Learnability,' 'Operability,' 'Understandability,' and 'Complexity.' ...
Any organization that intends to use component-based software development, like outsourcing software, must first evaluate existing components against system requirements to find the best fit among many alternatives. As a result, there should be a mechanism to help with decision-making. Our proposed methodology tries to select the best alternative among available components, using the best decision-making approach. As an integrated method for order preference, the methodology in this paper uses two well-known criterion decision-making procedures, namely Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). By analyzing and selecting the optimal solution among a variety of Out Sourcing (OS) modules, the new model design makes the decision-making process easier. We evaluated two software attributes and predicted which was more effective. In this case, the advantage of utilizing AHP is that it allows the developer to evaluate the structure of the OS selection problem and calculate weights for the chosen criteria. After that, the SAW technique is used to calculate the alternatives ratings for OS components. The integration strategy used in our model and the resulting preference indication, which is produced as an explicit numeric value.
... By evaluating e-learning, academic managers and decision makers can identify good or bad actions, detect errors, correct mistakes, perceive risks, and make excellent investments, thus allowing learners to study effectively (Roffe, 2002). Over the past decades, a considerable number of articles using different evaluation approaches have been published to evaluate e-learning by examining its influencing factors (Al-Samarraie et al., 2018;Hu, 2016;Mohammed et al., 2017;Yigit et al., 2014). ...
Multicriteria decision-making (MCDM) techniques have been widely adopted to evaluate the effectiveness of e-learning. However, the literature review has not kept pace with the rapid accumulation of knowledge in this field. This study systematically reviews the MCDM techniques applied in e-learning issues. In total, we reviewed 77 published studies selected from the Web of Science and Scopus databases. We classified the selected studies by the publication year, the authors' nationality, and the type of MCDM techniques examined. We further discovered that the majority of previous studies adopted Information System Success Model (D&M model) which was proposed by Delone and McLean in 1992. Due to limited features that were provided by the e-learning system back in the mid-2000, the original D&M model might not consider some significant factors such as students’ characteristics, instructors’ characteristics, user interface and learning community. The goals of this systematic literature review are to understand if the original D&M model is addressed in modern e-learning and to determine if new factors have emerged to evaluate e-learning’s effectiveness but not captured by the original D&M model. This review contributes three new theoretical perspectives. First, reclassification of the D&M model is conducted to include learners’ characteristics, instructors’ characteristics, user interface, and learning community. Second, this study discovers that there is a need to perform reclassification of overlapping subfactors. Third, this review also identifies that the dependencies among the factors in the D&M model are inadequately examined in previous studies. Researchers can utilize the findings of this study as a foundation to formulate their research frameworks, and practitioners can integrate the significant factors identified in this study into their decision-making processes when developing e-learning at their institutions.
... Step 7. Priority row ranking: Alternatives C can be ranked in descending order. 14 Considering the steps described above we solve problem of the selection of the gas flow meter [31]. ...
Problems. In many cases, the problem arises of choosing from a variety of alternatives to measuring instruments necessary for measuring the parameters of a technological process. So as that measuring instruments with different designs, operating principles and different technical and technological parameters are produced by different manufacturers. One of the important problems in the development of these systems is the selection of measuring instruments with optimal operational and metrological characteristics in accordance with the manufacturer's specifications and the terms of reference for the system design. Methodology. Multiple criteria decision-making (MCDM) are used in order to solve problems related to several criteria. The high accuracy sensors ensure accurate readings, although there are several parameters involved in measuring process: flow rate; gas or gases used; temperature; operating pressure; pressure drop; price; accuracy; response time; warm-up time; stable measurement range (turndown) gas is selected. Findings. Selection process of the optimal gas flow meter for information-measuring system is considered. Criteria for selection of the flow meters is studied, shown that the selection process is multi-criterial problem. Algorithm for selection of the gas flow meter is proposed. Proposed algorithm based on AHP method and TOPSIS. An idea of algorithm is following. İnitially experts select some alternatives, which meet all requirements for flow meters of different suppliers. At the next step the relative criteria of importance are calculated. After that decision matrixes are composed by which determine the preferences for the goals. Originality. Better alternative or flow meter with optimal parameters is determined by ranking of all alternatives. Practical value. Applying of the proposed algorithm increases reliability of the final decision and automates the selection process by using of the corresponding software.
... This work extracted nine different criteria for the model presentation from previous research studies. Yigit, Isik, and Ince [56] applied AHP to their study. The web-based software SDUNESA uses AHP's parameters for the selection of learning objectives. ...
E-Learning is a popular computer-based teaching–learning system that has been rapidly gaining global attention during and post COVID-19. The leaping changes in digital technology have enabled E-Learning to become more effective in recent years. It offers freedom from restrictions caused by geographical boundaries and provides time flexibility in the teaching–learning process. Apart from its numerous advantages, the success of E-Learning depends upon many critical success factors (CSFs) and barriers. If the barriers that lie in the way of successful E-Learning implementation are not addressed diligently, it will limit E-Learning success. It has been revealed through past research that these barriers are serious threats that need immediate attention in their redressal. This paper attempts to reveal sixteen barriers under four different dimensions by going through a comprehensive review of the literature and engaging decision makers. Furthermore, it uses the Analytic Hierarchy Process-Group Decision Making (AHP-GDM) methodology to evaluate and prioritize them. The results obtained show that barriers related to the Institutional Management Dimension (BIMD), Infrastructure and Technological Dimension (BITD), Student Dimension (BSD), and Instructor Dimension (BID) pose the greatest challenges in the successful implementation of E-Learning. The AHP-GDM methodologies reveal the comparative relationship among these barriers as BIMD > BITD > BSD > BID and further quantify their negative effects as 46.35%, 29.88%, 12.30%, and 11.4%, respectively, on successful E-Learning systems (‘>’ indicates comparative challenges).
... The increase in the usage rate of e-learning platforms depends on the richness of the portal's contents. The creation and reusability of learning content are ensured by effective content libraries (Yigit et al., 2014). Increased multimedia use, high media update speed and short waiting times increase students' attentions (Lin et al., 2011). ...
The COVID 19 pandemic not only affected our health and social life in many aspects, but it also changed the classical way of training in classrooms and education preferences of society. As a solution various e-learning platforms were developed and preferred by many educational institutions where the individuals had the opportunity to try the advantages of e-learning platforms. Since the COVID-19 pandemic is neither the first nor the last epidemic, e-learning attracts more attention than ever before and the need for e-learning platforms is expected to be more in the near future. Thus it is necessary to define all critical success factors determining the efficiency of e-learning systems. E-learning platforms have advantages as well as disadvantages and comparisons involve uncertainties and qualitative assessments. A systematic approach should be used to determine the platforms' dimensions, features and weights of critical criteria. The motivation of our study is to determine the weights of all critical success criteria and offer a reliable method for evaluating e-learning platforms. In this study, the interval type-2 fuzzy Analytical Hierarchy Process was utilized to compare critical success factors of e-learning platforms. This is the most comprehensive study considering all critical success factors of e-learning platforms as an Multi Criteria decision Making problem, where 11 criteria and 106 sub-criteria were defined, evaluated and prioritized. This study provides an acceptable rationale for evaluations of e-learning platforms and the results of this study can be used in real-world performance evaluations.
