Multi-criteria decision making approach in E-learning: A systematic review and classification

Improve the satisfaction of medical staff on the use of home nursing mobile APP by using a hybrid multi-standard decision model

OBJECTIVE

To identify critical satisfaction gaps in a home nursing mobile application (APP) using a systematic decision-making model.

METHODS

Initially, the decision-making trial and evaluation laboratory method was used to analyze the relationship structure and corresponding weights among the indicators. The Importance-Performance Analysis (IPA) method was used to identify the categories of all indicators and their corresponding strategic directions. Twenty-six home nursing specialists currently providing home nursing services were recruited for this study.

RESULTS

The IPA results revealed that "Assurance," "Reliability," and "Personal security protection" are critical satisfaction gaps. From the influence network and weight results, "information quality" and "system quality" were the critical quality factors in the home nursing mobile APP. The influence of the network relationship structure and weight demonstrated a 98.12% significance level, indicating good stability.

CONCLUSION

Continuous improvement in information and system quality is recommended to optimize the overall quality of the home nursing mobile APP. Additionally, user demands should be considered, and personal safety guarantee functions should be developed and integrated into the system to ensure the safety of home nursing workers.

Identification of critical quality factors and critical satisfaction gaps in emergency training courses for new nurses by the multi-criteria network structure model

OBJECTIVE

To identify critical quality factors and critical satisfaction gaps in emergency training courses for new nurses through a systematic decision-making model.

METHODS

Firstly, the service quality (SERVQUAL) was used in the evaluation index system of this study. Then, the decision-making trial and evaluation laboratory (DEMATEL) method was used to analyze the relationship structure and the corresponding weights between the indicators. Finally, the importance-performance analysis (IPA) method was used to identify the categories of all indicators and the corresponding strategic directions. Fifteen new nurses in Taizhou Hospital of Zhejiang Province were selected as participants in this study.

RESULTS

The IPA results showed that "(C₁₃)," "(C₂₂)," "(C₅₂)," "(C₅₃)," "(C₅₄)," "(C₅₅)," "(C₅₆),"and "(C₅₇)" are critical satisfaction gaps. From the results of influence network and weight, empathy (C₅) was the critical quality factor of the entire training course. The influence network relationship structure and weight had a 98.1% significant confidence level, indicating good stability.

CONCLUSION

Teachers' empathy is key to the learning outcomes of new nurses in emergency nursing training courses. Hence, teachers should be attentive to the empathetic quality of their teaching methods to help new nurses gain knowledge and experience in emergency care, especially when they come from different professions and departments.

An information system success model for e-learning postadoption using the fuzzy analytic network process

The underutilization of e-learning among university lecturers is an important issue that needs to be resolved. This study aimed to formulate an e-learning postadoption model for Malaysian universities. Data were collected using self-administered questionnaires involving 36 e-learning experts who from lecturers in public and private universities in Malaysia. The data collected was then analyzed using the extent analysis method proposed by Chang (European Journal of Operational Research, 95(3), 649-655, 1996) to examine the weights and rankings of the factors and subfactors. This study showed that for e-learning postadoption, the most important factor is institution service quality, followed by system quality, content quality, instructors' characteristics, and learners' characteristics. This study extends the information systems success model into the e-learning postadoption context. In particular, this study offered insights concerning the dependencies among the factors in the model within the Malaysian university context. The findings are useful for the long-range strategic management of university administrators, and the model can be adopted as a reference to form a rating system to analyze e-learning postadoption. University administrators can analyze critical factors that increase e-learning's post adoption and lead to more efficient resource allocation and management of e-learning.

A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations

Distance learning has been adopted as an alternative learning strategy to the face-to-face teaching methodology. It has been largely implemented by many governments worldwide due to the spread of the COVID-19 pandemic and the implication in enforcing lockdown and social distancing. In emergency situations distance learning is referred to as Emergency Remote Teaching (ERT). Due to this dynamic, sudden shift, and scaling demand in distance learning, many challenges have been accentuated. These include technological adoption, student commitments, parent involvement, and teacher extra burden management, changes in the organization methodology, in addition to government development of new guidelines and regulations to assess, manage, and control the outcomes of distance learning. The objective of this paper is to analyze the alternatives of distance learning and discuss how these alternatives reflect on student academic performance and retention in distance learning education. We first, examine how different stakeholders make use of distance learning to achieve the learning objectives. Then, we evaluate various alternatives and criteria that influence distance learning, we study the correlation between them and extract the best alternatives. The model we propose is a multi-criteria decision-making model that assigns various scores of weights to alternatives, then the best-scored alternative is passed through a recommendation model. Finally, our system proposes customized recommendations to students, and teachers which will lead to enhancing student academic performance. We believe that this study will serve the education system and provides valuable insights and understanding of the use of distance learning and its effectiveness.

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Purpose

The scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.

Design/methodology/approach

This article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.

Findings

The authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.

Social implications

Cultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.

Originality/value

The method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within “e-learning in higher education.” E-learning as a concept has been widely used in the academic and professional communities and has been approved as an educational approach during COVID-19. This article starts with a literature review of e-learning. Diverse subjects have appeared on the topic of e-learning, which is indicative of the dynamic and multidisciplinary nature of the field. These include analyses of the most influential authors, of models and networks for bibliometric analysis, and progress towards the current research within the most critical areas. A bibliometric review analyzes data of 602 studies published (2020–2021) in the Web of Science (WoS) database to fully understand this field. The data were examined using VOSviewer, CiteSpace, and KnowledgeMatrix Plus to extract networks and bibliometric indicators about keywords, authors, organizations, and countries. The study concluded with several results within higher education. Many converging words or sub-fields of e-learning in higher education included distance learning, distance learning, interactive learning, online learning, virtual learning, computer-based learning, digital learning, and blended learning (hybrid learning). This research is mainly focused on pedagogical techniques, particularly e-learning and collaborative learning, but these are not the only trends developing in this area. The sub-fields of artificial intelligence, machine learning, and deep learning constitute new research directions for e-learning in light of COVID-19 and are suggestive of new approaches for further analysis.

Selection of suitable distance education platforms based on human–computer interaction criteria under fuzzy environment

The rapid spread of the COVID-19 pandemic has affected not only the health industry but also the education sector. E-learning systems have recently become a compulsory part of all education institutions, including schools, colleges, and universities worldwide because of the COVID-19 pandemic crisis. The objectives of the current study were twofold: (1) to conduct an analytical approach for ranking of distance education platforms based on human–computer interaction criteria and (2) to identify the most appropriate distance learning platform for teaching and learning activities by using multi-criteria decision-making approaches. Selection criteria were grouped into human–computer interaction-related criteria, such as ease of use, possibility of causing mental workload, user-friendly interface design, presentation method, and interactivity. In the selection procedure, a spherical fuzzy extension of Analytical Hierarchy Process was utilized to identify the weights of selection criteria and to rank distance education platforms. The results revealed that the most important criterion was the possibility of causing mental workload while the most preferable e-learning system was identified as “A3”.

Medical image processing and COVID-19: A literature review and bibliometric analysis

COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.

E-Learning Platform Assessment and Selection Using Two-Stage Multi-Criteria Decision-Making Approach with Grey Theory: A Case Study in Vietnam

Education has changed dramatically due to the severe global pandemic COVID-19, with the phenomenal growth of e-learning, whereby teaching is undertaken remotely and on digital platforms. E-learning is revolutionizing education systems, as it remains the only option during the ongoing crisis and has tremendous potential to fulfill instructional plans and safeguard students’ learning rights. The selection of e-learning platforms is a multi-criteria decision-making (MCDM) problem. Expert analyses over numerous criteria and alternatives are usually linguistic terms, which can be represented through grey numbers. This article proposes an integrated approach of grey analytic hierarchy process (G-AHP) and grey technique for order preference by similarity to ideal solution (G-TOPSIS) to evaluate the best e-learning website for network teaching. This introduced approach handles the linguistic evaluation of experts based on grey systems theory, estimates the relative importance of evaluation criteria with the G-AHP method, and acquires e-learning websites’ ranking utilizing G-TOPSIS. The applicability and superiority of the presented method are illustrated through a practical e-learning website selection case in Vietnam. From G-AHP analysis, educational level, price, right and understandable content, complete content, and up-to-date were found as the most impactful criteria. From G-TOPSIS, Edumall is the best platform. Comparisons are conducted with other MCDM methods; the priority orders of the best websites are similar, indicating the robust proposed methodology. The proposed integrated model in this study supports the stakeholders in selecting the most effective e-learning environments and could be a reference for further development of e-learning teaching-learning systems.

Conditions and Determinants of Distance Education for Students during the COVID-19 Pandemic—Evaluation in the Kuyavia-Pomerania Region in Poland

This research project is the first and only comprehensive regional study in Poland including all teachers and headteachers of primary and secondary schools. The aim of the study was to determine the factors influencing the learning process carried out remotely and to examine the potential of this form of education for primary and secondary school students and its future use to improve the quality and accessibility of education. An important aspect of this research was also the assessment of the respondents’ capabilities and competences in the area of operating and using digital tools in the educational process. During the COVID-19 pandemic, a total of 9070 teachers took part in this study. The method of data collection was an online survey. The research instrument consisted of a survey questionnaire. The survey was conducted electronically using tools provided by Google. The following methods were used to evaluate the data: count tables, which allowed for data exploration; a graphical method—cross-sectional analysis; and individual correlation analysis, which allowed for preliminary data evaluation. The results obtained within the research process conducted allowed us to achieve our goals. According to 42.4% of the teachers surveyed, remote learning is a good alternative to traditional teaching. The study also confirmed, inter alia, the presence of a correlation between technical conditions of classes and specific forms and levels of education, with 49.7% of respondents indicating that remote learning depends mainly on the quality of technical parameters, including a good or very good Internet connection. The biggest advantages of e-learning, as indicated by the respondents, were the possibility of working from home, conveniently sharing educational materials, and utilising additional teaching aids. Health issues and long times spent in front of the computer were highlighted as the greatest drawbacks of this teaching method. The results also showed that, as the age of the teachers increases, the acceptance and trust in distance learning activities increases while the fear of using this form of student learning decreases. This study lays the foundation for designing systemic, nationwide solutions, and thus, the authors are planning to continue their research in the context of comparative analysis of different regions and to analyse this process in the context of the entire country. The analyses performed form a part of a global line of research in the field of education and in the implementation of innovative tools and forms of education provision.

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

A STEM Course Analysis During COVID-19: A Comparison Study in Performance and Affective Domain of PSTs Between F2F and F2S Flipped Classroom

Due to the worldwide COVID-19 pandemic, university education has faced a significant challenge that requires adaptation to virtual and online education. Here, a fruitful flipped methodology with increased popularity can support adaption to and improvement of the current pandemic situation. This research presents a comparison of two different instruction situations with an identical teaching methodology, face-to-face (F2F) and face-to-screen (F2S) flipped methodology, in terms of students' performance and affective domain in a science, technology, engineering and mathematics (STEM) course. It was considered and designed as an examination of 132 pre-service teachers (PSTs), with 68 and 64 PSTs respectively for each group. The first group before the pandemic was applied by F2F flipped classroom and the second group after the pandemic was applied by F2S flipped classroom. The results after pertaining various data analyses of class activities and questionaries showed that performance had been improved for both groups toward the course. In addition, F2F had a significant difference in PSTs' emotion and perception toward the course and made classes more interactive. The mean score values of students' emotion and perception between two groups showed that the difference between these mean values were significant, suggesting a very large effect. Particularly, the effect size (ES) showed that positive emotions were more significant with different variables and the items Q7-Q9 of questionnaires indicated more significant different perceptions for both F2F and F2S after completing the course. Finally, the principal component analysis (PCA) test described that F2F answers were located mainly in the positive emotion, while F2S answers were grouped in the negative emotion, while no differences were observed for PSTs perceptions to the flipped methodology. Consequently, although F2F-F2S transition was an effective process, instructors and PSTs faced difficulties in the platform usage for online lectures reflecting emotions' results in F2S group. Thus, by solving the problems raised, it will allow PSTs to be more interactive in a virtual and online context for their future implementation by giving them active instruction methodology and educating future students to teach STEM contents.

Prioritization of Influence Factors for Selecting E–Learning Systems

COVID-19 pandemic affects not only daily life activities but also traditional education systems. Based on the current developments, to stick by their academic calendars, most of the educational institutions continue their classes via online channels. Since the selection of the most appropriate e–learning platform depends on multi–criteria, the evaluation of this selection process can be dealt with decision support systems. In this study, cognitive mapping extended with intuitionistic fuzzy sets is introduced for prioritizing the e–learning platform selection factors under fuzzy environment based on the multi–expert judgments. Based on the results, infrastructure and ease of use are determined as the most effective factors. For further studies, a sensitivity analysis based on the initial vector determination can be studied to check its effect on the outputs.

A Multiple Criteria Decision Making Approach to Designing Teaching Plans in Higher Education Institutions

The involvement of competences in the teaching–learning planning process in Higher Education is essential for their success in the European Higher Education Area. This study presents a participatory multi-criteria model based on Voting Analytic Hierarchy Process (VAHP) analysis, focusing on the attainment of competences that permits consensus between lecturers and students in the design of teaching plans using two assessments: the assessment of competences by students and the lecturers’ assessment of the contribution of teaching strategies to the attainment of each competence. To validate the methodology, a survey was carried out to determine the preferences of 211 students on the competences of a quantitative subject in several business degrees and a survey of 11 lecturers to assess the contribution of the teaching strategies in the acquisition of each competence. The results show that practical lessons and group work should receive more importance in the teaching plans of the subject Financial Management according to the defined competences, the students’ preferences and the criterion of each lecturer for each teaching strategy. The results show the applicability of the participatory methodology proposed to formally agree on the design of teaching plans in higher education organizations between lecturers and students.

Assessment of sustainability science education criteria in online-learning through fuzzy-operational and multi-decision analysis and professional survey

Sustainable development in education is growing a public awareness and is gaining an increasing importance to improve a long-term learning program. However, a higher sustainability education is an initial phase as ever in many universities. E-learning systems in science education based on innovative information and Information and Communication Technologies (ICTs) have close relationship in effective long-standing learnings of sustainability and numerous criteria. Also, the technological challenge integrated to sustainability science education has been focused on e-learning programs, which can fill current educational niche. This paper presents a combined application, Fuzzy-DEcision-MAking Trial and Evaluation Laboratory/Multi-Criteria Decision Analysis (F-DEMATEL/MCDA) technique, identifying and analyzing e-learning systems' the most essential criteria for sustainability science education. The main criteria to accomplish this goal proposed are delineated, weighted, appraised and assigned into four groups such as sustainability, science education, e-learning and technology criteria. Sixteen sub-criteria were analyzed by the participatory F-DEMATEL technique on the basis of coefficients' value and computation in the context of impact and examination. For the final decision problem, the most important criteria are acquired with the help of Weighted Linear Combination (WLC) assessed by six implementation strategies’ sensitivity analysis (A to F) and the sub-criteria by a professional online survey. Analysis on F-DEMATEL/MCDA shows sustainability criteria (Strategy A, 0.54 with 84% most likely and likely professional perception survey) that is the most essential criteria in sustainability and e-learning systems science education. Also, it indicates information environmental (0.57 with 77% most likely and likely professional perception survey) is the most essential variable among sub-criteria. Strategy F considering the most important criteria as equal (0.25) got 19% positively perception professional assessment. Consequently, fuzzy-operational and multi-decision analysis and professional survey could be utilized to find out the most essential sustainability science e-learning criteria that also could be engaged to make pliable and pertinent decision features.

Trends in Educational Research about e-Learning: A Systematic Literature Review (2009–2018)

The concept of e-learning is a technology-mediated learning approach of great potential from the educational perspective and it has been one of the main research lines of Educational Technology in the last decades. The aim of the present systematic literature review (SLR) was to identify (a) the research topics; (b) the most relevant theories; (c) the most researched modalities; and (d) the research methodologies used. To this end, the PRISMA protocol was followed, and different tools were used for the bibliographic management and text-mining. The literature selection was carried out in three first-quartile journals indexed in JCR-SSCI specialized in Educational Technology. A total of 248 articles composed the final sample. The analysis of the texts identified three main nodes: (a) online students; (b) online teachers; and (c) curriculum-interactive learning environments. It was revealed that MOOC was the most researched e-learning modality. The Community of Inquiry and the Technological Acceptance Model, were the most used theories in the analyzed studies. The most frequent methodology was case study. Finally, the conclusions regarding the objectives of our SRL are presented: Main themes and research sub-themes, most researched e-learning modality, most relevant theoretical frameworks on e-learning, and typologies of research methodologies.

Adapting to PSTs’ Pedagogical Changes in Sustainable Mathematics Education through Flipped E-Learning: Ranking Its Criteria with MCDA/F-DEMATEL

Mathematics education for sustainable development is perceived as a core keystone, although its concept is related to its typical issue of ecology and economy in the educational realm. Thus, through current information and communication technology (ICT) impacts, altering pedagogy is highly conflicted in teaching/learning mathematics. This research attempted to classify and investigate criteria for mathematics education with a multi-criteria decision analysis/fuzzy-decision-making trial and evaluation laboratory (MCDA/F-DEMATEL) method in the context of the system of flipped e-learning. In particular, the sustainable development of this research focus is in mathematics education in view of pre-service teachers (PSTs) adapting to their pedagogical changes. With the MCDA/F-DEMATEL, the main criteria and sub-criteria were selected after the consultation of a group of experts as follows: mathematics education, sustainable development, and flipped-e-learning technology criteria. Then, with fifteen sub-criteria, the definitive analyses results were gauged with simple additive weighting (SAW) along with their weight coefficients’ calculation, sensitivity analysis (i–v scenarios), and a professional survey. The results described as the most important criteria for adapting to PSTs pedagogical changes in sustainable mathematical education through a flipped e-learning system were the flipped e-learning technology criteria (scenario i), with an 83% positive perception by professionals’ survey; among its sub-criteria, information technology usage contents (0.43) was the most affected variable, with a 42% very likely perception. Scenario iii, which was the slightly more preferred criteria than mathematics education, got a 78% positive perception from professionals’ survey. Therefore, this proposed methodology could be employed to validate the most important sustainable mathematics with flipped e-learning criteria for adapting to PSTs’ pedagogical changes with corresponding education contexts in more long-term learning.

Minimizing the Environmental Impact of Industrial Production: Evidence from South Korean Waste Treatment Investment Projects

This research deals with the theoretical and practical issues of investment support activities for industrial waste management in developed countries, based on the example of South Korea. The main goal of this research is the evaluation of waste treatment investment projects and understanding their impact on the development of environmental policies. The problems of forming the sustainable systems for controlling the disposal of industrial wastes are being studied. The authors discuss the practical application of environmental policies and modern technologies of South Korean companies in the field of industrial waste processing. The approaches of waste investment project’s evaluation are applied and multi-criteria decision making (MCDM) methods were discussed for various cases and applications. Using MCDM methods, the authors study the effectiveness of investment projects in waste treatment activities in Korea. The analyses of MCDM methods are implemented in this research to provide some instructions on how to effectively apply these methods in waste treatment investment project analyses. Furthermore, the authors propose a combination of multi-criterial selection and interval preferences to evaluate waste treatment projects. The proposed approach improves the method of calculating economic efficiency based on a one-dimensional criterion and sensitivity analysis. The main results of this research perform the investment impact and risk-analysis on the environmental policies development.

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

Prioritizing Elements of Science Education for Sustainable Development with the MCDA-FDEMATEL Method Using the Flipped E-Learning Scheme

Selecting and ordering components for sustainable science education is a critical issue, which is presently obtaining increased attention because of being at an early stage and scarce application in higher education. Though the flipped e-learning scheme is one of the novel information and communication technologies (ICTs), it can be of great relevance in a long-term learning program for various sustainable science education criteria. This research presents an approach to identify and analyze elements for science education for sustainable development with multi-criteria decision analysis-fuzzy decision-making trial and evaluation laboratory (MCDA-FDEMATEL) method by flipped e-learning system. With the method proposed, the main elements are collected as science-education, sustainable-development, technology-infrastructure and flipped-e-learning elements. The final results’ analyses with sixteen sub-elements are assessed with weighted linear combination (WLC) and sensitivity-analysis (I to VI implementations) in the context of the MCDA-FDEMATEL method. The most important element and sub-element for science education for sustainable development through flipped e-learning teaching are sustainable-development (as an element), VI implementation with 0.540 weight, and environmental contents (as a sub-element) with 0.570 weight. Consequently, this proposed approach could be used in different studies to validate the most important aspects of science education for sustainable development through flipped e-learning teaching elements and sub-elements with equivalent and comparable education settings.

Carbon dioxide (CO2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017

Understanding the nexus CO₂ emissions and economic growth helps economies in formulating energy policies and developing energy resources in sustainable ways. Although during recent years, numerous of the previous studies have been very thoroughly investigated the nexus between economic growth and CO₂ emissions, there is a lack of research regarding the qualitative systematic review and meta-analysis in these areas. The main purpose of this review paper is to present the comprehensive overview of the relationship between CO₂ emissions and economic growth. In this regard, the Web of Science database has been chosen and a qualitative systematic and meta-analysis method which called "Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)" has been proposed. Therefore, a review of 175 published articles appearing in 55 scholarly international journals between 1995 and 2017 has been achieved to reach a broad review of the nexus between economic growth and CO₂ emissions with other indicators. Consequently, the selected articles have been categorized by the author name, the year of publication, data duration, types of techniques, data analysis method, the name of indicators, country, scope (individual country and multi-countries), journals, results, and outcome in which they appeared. The results of this paper demonstrated that the nexus between CO₂ emissions and economic growth gives reasons for policy options that have to reduce emissions by imposing limiting factors on economic growth as well. Given the fact that bidirectional causality exists, as far as economic growth increases or decreases, further CO₂ emissions are stimulated in higher or lower levels and consequently, a potential reduction of the emissions should have an adverse influence on economic growth.

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

Measurement of environmental and energy economics presents an analytical foundation for environmental decision making and policy analysis. Applications of data envelopment analysis (DEA) models in the assessment of environmental and energy economics are increasing notably. The main objective of this review paper is to provide the comprehensive overview of the application of DEA models in the fields of environmental and energy economics. In this regard, a total 145 articles published in the high-quality international journals extracted from two important databases (Web of Science and Scopus) were selected for review. The 145 selected articles are reviewed and classified based on different criteria including author(s), application scheme, different DEA models, application fields, the name of journals and year of publication. This review article provided insights into the methodological and conceptualization study in the application of DEA models in the environmental and energy economics fields. This study should enable scholars and practitioners to understand the state of art of input and output indicators of DEA in the fields of environmental and energy economics.