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For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

Prospective students and other stakeholders in the education system use global and national rankings as a measure of the quality of education offered by different higher educational institutions. The ranking of an Institution is seen as a measure of reputation and has a significant role in attracting students. But are students happy in the top-ranked institutions? Does a high rank translate into high student satisfaction? This study answers this question taking data from top educational institutions in India.

This study examines how the top-ranked higher education institutions in India fare on student satisfaction. Using the data on key performance indicators published by the National Institutional Ranking Framework (NIRF) and student satisfaction scores of these institutions reported by NAAC, the study explores a possible relationship between the ranking of an institution and its student satisfaction score.

The study finds no significant relationship between the ranking of an institution and its student satisfaction score. The only institutional performance dimension which has a positive correlation with student satisfaction is graduate outcome. The diversity dimension is seen to be negatively correlated with student satisfaction.

The importance of modifying the ranking frameworks to account for the real drivers of student satisfaction is highlighted. The items in the student satisfaction survey should be regularly updated to reflect the actual concerns of the students. This is very important given the fact that the number of Indian students going abroad for higher education recorded a six-year high in 2022 at 750,365.

With more than 50,000 institutions catering to over 40 million students, India has the largest higher education system in the world. Given the high level of competition among these institutions, ranking and accreditation have become important parameters used by students for selection of an institution. But do top-ranked higher education institutions have the most satisfied student community? The assumption is disproved using the most credible secondary data. This study is the first of its kind in the Indian context. It has huge implications for the most respected ranking frameworks.

This study aims to explore how university language students evaluate different sustainability themes and examine the overall relevance of ecological, social, cultural and economic sustainability dimensions in language education.

A questionnaire was designed to study Finnish university language students’ (n = 55) order of priority for sustainability dimensions and their sub-themes and the justifications for the priority orders using a mixed methods design. Qualitative content analysis was conducted using NVivo software, and weighted rankings were used to analyse the quantitative data.

The findings of the study showed that language students evaluated the social and cultural dimensions as the most relevant in language teaching. In all dimensions, students approached sustainability mainly by prioritising larger issues and advancing towards smaller ones. Most non-directional responses appeared in the economic dimension. In addition, individual prioritising and justification approaches varied between different sustainability dimensions.

To the best of the authors’ knowledge, no previous studies have examined language students’ evaluations of and justifications for all four sustainability dimensions. The results highlight the need to use multiple, holistic approaches and systems thinking to incorporate education for sustainable development.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

This paper aims to define a hierarchical and multi-criteria framework based on pillars of the Modernization of Higher Education to evaluate European Accounting Doctoral Programmes (EADE-Model).

The authors applied a quali-quantitative methodology based on the analytic hierarchy process and the survey approach. The authors conducted an extensive literature and regulation review to identify the dimensions affecting the quality of Doctoral Programmes, choosing accounting as the relevant and pivotal field. The authors also used the survey to select the most critical quality dimensions and derive their weight to build EADE Model. The validity of the proposed model has been tested through the application to the Italian scenario.

The findings provide a critical extension of accounting ranking studies constructing a multi-criteria, hierarchical and updated evaluation model recognizing the role of doctoral training in the knowledge-based society. The results shed new light on weak areas apt to be improved and propose potential amendments to enhance the quality standard of ADE.

Theoretical and practical implications of this paper are directed to academics, policymakers and PhD programmes administrators.

The research is original in drafting a hierarchical multi-criteria framework for evaluating ADE in the Higher Education System. This model may be extended to other fields.

We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students.

The paper explores using poetry as a research method to reveal the learning experiences of adult learners, who have often had disruptive experiences of the formal schooling system and return to study in community-based education spaces. Inspired by Laurel Richardson’s transgressive technique of presenting sociological data through poetry as method, we use poetic representations of these learners' lives alongside case study research methodology. The research was conducted in conjunction with Neighbourhood Houses in Victoria, Australia. Qualitative data were generated through conducting multiple case studies of learners across various adult community education (ACE) sites. In this research, some case studies were presented in the traditional method of writing biography, others were written in the form of found poetry, which we refer to as data as poetry and text. The paper uses found poetry through participant-voiced poems written from interview transcripts. We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students. Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research. These poetic representations of data reveal learner experiences in an embodied and agentic way while providing readers with a deep and rich understanding of these crucial adult learning spaces.

Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research.

This research paper is empirical research and has not been submitted elsewhere for publication.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

Supply chain disruptions have a significant impact on overall project delivery. This study aims to identify the supply chain disruptive factors and develop a framework to mitigate the disruptive effects on the supply chain. Covid-19 and Brexit disruption and their longevity effects in the short, medium and long term on the supply chain are relied upon to develop the framework.

The study adopted a mixed-method approach with a sequential explanatory design. The main disruptive factors were identified through a literature review, and key factors were selected through a focus group exercise. A questionnaire survey was carried out to sample opinions from the practitioners; 41 questionnaires were received and analysed using the relative importance index (RII) method for ranking the factors and percentage frequency distribution to determine the longevity effects. Five follow-up semi-structured interviews were conducted over the telephone and later transcribed.

The results of Covid-19 disruption indicate that material cost increase ranked first (RII: 0.863), logistics cost increase and supply chain interaction ranked second and third, respectively. They have long-term, medium-term and short-term longevity effects, respectively. The lowest-rated factors were communication (RII: 0.561), staff shortages (RII: 0.629) and impact on relationships (RII: 0.639). The three most ranked Brexit disruptive factors are supply chain interaction (RII: 0.775), material cost increase (RII: 0.766) and logistic and haulage delay (RII: 0.717). The first two factors have long-term effects, and the logistics and haulage delays have a medium-term impact. The mitigating solutions suggested in the framework are collaborative working, stronger resilience to external forces and better transparency and communication that will lead to good relationships among the supply chain members.

The scope of the study was limited to the UK construction industry; however, the pandemic effect on supply chain can serve as critical learning curve in other developed and developing countries.

The study will help the government and construction firms to understand the focal areas of importance in solving the supply chain disruption problems based on the effects of Brexit and Covid-19. The research would be useful in ensuring the proactive involvement of the government and contracting firms in their preparedness for similar events in the future. The results could be interpreted for critical learning in other developed/developing countries.

Identifying and ranking the supply chain disruptive factors affecting the small‐ and medium‐sized enterprises (SMEs) in the UK construction industry has been the focal point of this study. The study also proposes a simple but effective framework comprising the highly ranked factors, their longevity effects and mitigating measures. This will help the SMEs manage future/similar external events affecting the supply chain.

This study aims to focus on key approaches to education for sustainability (EfS) leadership development in the context of Malaysian and Japanese universities. The authors identify key indicators of effective EfS leadership development approaches using both descriptive and inferential analyses, identify and compare the preferred leadership learning methods of academics and examine the impact of marital status, country of residence and administrative position on the three EfS leadership development approaches.

The study is quantitative in approach and survey in design. Data were collected from 664 academics and analysed using the efficient partial least squares (PLSe2) methodology. To provide higher education researchers with more analytical insights, the authors re-estimated the models based on the maximum likelihood methodology and compared the results across the two methods.

The inferential results underscored the significance of four EfS leadership learning methods, namely, “Involvement in professional leadership groups or associations, including those concerned with EfS”, “Being involved in a formal mentoring/coaching program”, “Completing formal leadership programs provided by my institution” and “Participating in higher education leadership seminars”. Additionally, the authors noted a significant impact of country of residence on the three approaches to EfS leadership development. Furthermore, although marital status emerged as a predictor for self-managed learning and formal leadership development (with little practical relevance), administrative position did not exhibit any influence on the three approaches.

In addition to the theoretical and methodological implications drawn from the findings, the authors emphasize a number of practical implications, namely, exploring the applicability of the results to other East Asian countries, the adaptation of current higher education leadership development programmes focused on the key challenges faced by successful leaders in similar roles, and the consideration of a range of independent variables including marital status, administrative position and country of residence in the formulation of policies related to EfS leadership development.

This study represents an inaugural international comparative analysis that specifically examines EfS leadership learning methods. The investigation uses the research approach and conceptual framework used in the international Turnaround Leadership for Sustainability in Higher Education initiative and uses the PLSe2 methodology to inferentially pinpoint key learning methods and test the formulated hypotheses.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.