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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.

This paper aims to manage the word gap in information retrieval (IR) especially for long documents belonging to specific domains. In fact, with the continuous growth of text data that modern IR systems have to manage, existing solutions are needed to efficiently find the best set of documents for a given request. The words used to describe a query can differ from those used in related documents. Despite meaning closeness, nonoverlapping words are challenging for IR systems. This word gap becomes significant for long documents from specific domains.

To generate new words for a document, a deep learning (DL) masked language model is used to infer related words. Used DL models are pretrained on massive text data and carry common or specific domain knowledge to propose a better document representation.

The authors evaluate the approach of this study on specific IR domains with long documents to show the genericity of the proposed model and achieve encouraging results.

In this paper, to the best of the authors’ knowledge, an original unsupervised and modular IR system based on recent DL methods is introduced.

The purpose of this study is to examine the impact of country-level governance on sustainability performance, taking into account the effect of sustainable development goals (SDGs) and board characteristics.

This study uses panel data analysis using fixed effect models to investigate the influence of country-level governance on sustainability performance while considering the effect of SDGs and board characteristics. The sample comprises 8,273 firms across 41 countries during the period spanning from 2016 to 2021. The sample is divided into two categories based on the score of SDGs.

The findings of this study show that countries with high SDGs score have better overall country-level governance and board attributes which have a statistically significant positive impact on sustainability performance. However, for those countries with low SDGs, political stability shows a statistically insignificant and negative impact on sustainability performance, while government effectiveness indicates a statistically insignificant positive impact on sustainability performance.

This study contributes to the literature by providing empirical evidence on the relationship between country-level governance, SDGs, board characteristics and sustainability performance. The study also highlights the importance of considering the effect of SDGs on the relationship between country-level governance and sustainability performance. The findings of this study could be useful for policymakers and firms in improving their sustainability performance and contributing to sustainable development.

This study examined the factors impeding the implementation of micro-credentials and accepting it as a credible source of earning professional qualifications and certifications necessary for pursuing higher education or other career goals.

The factors were identified by reflecting on the recent literature and Internet resources coupled with in-depth brainstorming with experts in the field of micro-credentials including educators, learners and employers. Two ranking methods, namely Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE) and multi-objective optimization based on ratio analysis (MOORA), are used together to rank the major challenges.

The results of this study present that lack of clear definitions, ambiguous course descriptions, lack of accreditation and quality assurance, unclear remuneration policies, lack of coordination between learning hours and learning outcomes, the inadequate volume of learning, and lack of acceptance by individuals and organizations are the top-ranked and the most significant barriers in the implementation of micro-credentials.

The findings can be used by educational institutions, organizations and policymakers to better understand the issues and develop strategies to address them, making micro-credentials a more recognized form of education and qualifications.

The novelty of this study is to identify the primary factors influencing the implementation of micro-credentials from the educators', students' and employers' perspectives and to prioritize those using ranking methods such as PROMETHEE and MOORA.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

The internationalisation of industries has spilled over to academia, whereby institutions of higher learning (IHL) increasingly compete in the graduate quality and applied graduate knowledge capabilities that they can offer. With increasing global competition for students, combined with the evolving need for lifelong learning in dynamic industries impacted by digital knowledge management, there is an opportunity for IHLs to be able to evolve to ensure their business models enable services and service delivery to cater to and help shape industry demands. This chapter will look at micro-credentialing (MC) and how the provision of MCs has changed along with the evolving IHL education environment. The demands of students, employers and ecosystem considerations will be addressed through a review of the current landscape, pathways to MC and how MC may be operationalised. The Bersteinian approach to pedagogic classification, which identifies the framework of knowledge as being communicable via three axes of singularism, regionalism and a wider generalist approach is referenced as a framework. The resultant recommendations that draw upon these foundations will conclude the chapter.

This paper explores who among the AACSB categorization of academics conducts the scholarship of teaching and learning (SoTL) research within business schools and how AACSB-accredited business schools capture SoTL research as part of their portfolio of intellectual contributions.

This study adopts a qualitative-method research design by collecting primary data through surveys, semi-structured interviews and secondary data in policy documents focused on AACSB-accredited business schools in Canada and the United States.

The findings establish that scholarly and practice academics who possess rigorously acquired research skills due to their terminal degrees are most likely to conduct SoTL research. The results also reveal an even split among respondents regarding whether their AACSB-accredited business school captures SoTL with their journal ranking frameworks.

Based on the findings, two recommendations are offered to foster more SoTL research at AACSB-accredited schools. First, higher education leaders (e.g. business school deans) can further inculcate a culture of SoTL research at the department and institutional levels by creating communities of practice (CoPs). Second, AACSB-accredited business schools could adopt more inclusive journal ranking frameworks to capture better and incentivize SoTL research.

This is the first known study to explore how AACSB Standards 3 and 8 are implemented and operationalized regarding SoTL research. Understanding how these standards are adopted and implemented could help institutional leaders, standard setters and administrators better facilitate SoTL research.

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.

Gamification is a booming motivational approach in information systems. Leaderboards play a key role in gamification; however, there are mixed findings regarding the heterogeneous motivational impacts of leaderboard positions. This study aims to clarify the motivational effects of high and low leaderboard positions by assembling diverse behavioral measures and self-reports. The measures used in this study shed a light on the quantitative and qualitative dynamics of motivation facilitated by leaderboard positions. The authors inspect motivation in relation to satisfaction and frustration of competence need.

The authors conducted an online experiment set in a crowdsourcing context, asking the participants to compete in an image tagging game. Participants' leaderboard positions were manipulated to be either high or low for five consecutive rounds. The number of clicks, tags, duration of tagging and persistence on the task were measured as indicators of motivation.

High ranks on leaderboards induced complacent behaviors choosing easy ways to maintain their positions, while low ranks led the participants to stick to the right process of the task with intensified motivation round after round. However, neither of the motivations seemed to be of intrinsic nature.

The present study provides conclusive evidence on the varying motivational impact of leaderboard positions. The authors also demonstrate how the “needs-as-motive” model (Sheldon and Gunz, 2009) applies to gamification. Its implications in self-determination theory and gamification literature are discussed.