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During the last few years there has been an increase of interest in work-based learning (WBL), which can be understood as a process of both developing workplace skills and promoting labor force productivity. This paper aims to map the scientific landscape related to WBL research worldwide.

combined bibliometrics and network analysis techniques to analyze data of scientific publications related to WBL indexed at the Web of Science (WoS) Core Collection.

results show an increase of publications over time: Education & Educational Research as the most frequent research area to which the articles were assigned, the UK and Australia as the main countries and Monash University (Australia) and Middlesex University (England) as the main organizations producing knowledge on WBL.

By offering a global scientific landscape of WBL research published so far, the authors aimed to contribute to future academic debates and studies in this field of knowledge.

Prior to the onset of the COVID-19 pandemic, women did nearly three-quarters of the world’s unpaid work. As institutional supports, including in-person school and community-based care for children, the elderly, and the disabled vanished early in the pandemic, many women’s caregiving responsibilities increased. In some cases, opportunities for paid employment disappeared due to layoffs and furloughs, while in others, paid work was no longer possible without access to the missing institutional supports. Either way, access to needed supports – financial, practical, and social – was diminished. The lapse of needed supports also had severe impacts on subgroups of women, including pregnant and post-partum women. A range of considerations – vaccine safety, social interaction and infection risk, disease severity – have posed serious challenges for pregnant and post-partum women. Across the board, women’s need for continuous access to better social, financial, and practical supports at home, in the community, and in the workplace was made even more evident by the COVID-19 pandemic.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters.

The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework's applicability.

The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making.

The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation's capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results.

Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies.

Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.