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Pulse check on discipline degrees for changed status quo is vital to ensure global futures for international enrolments (IEs). While employers spend less on training and more on innovating, can IEs manage time spent wisely and profitably (self-sufficiency) via collecting demand-driven credentials (micro-credentialing, Mc)? Due to limited research on Multidisciplinary, Micro-credentialing (MdMc), communication among stakeholders becomes difficult – there is no sense of self-sufficiency and course crossbreed lags; thus, diploma initiatives rarely succeed. Hence, MdMc aims to generate industry-necessitated, new knowledge hybrids where courses could generate adaptable Md links and intersections towards self-sufficiency. We propose a methodology based on Md content analysis on rapidly deployable knowledge bases suitable for multisector employability: a market survey to identify new knowledge areas. The outcome is to be knowledge mapped to identify gaps in skills required for applications to meet across disciplines. Finding the nature of these gaps intends to present possible knowledge links and intersections among courses. Diagrammatised and textual analysis of self-sufficiency-related benefits that could forge robust faulty-industry partnerships will be discussed – to demonstrate fluidity between credentials and careers. The resulting MdMc rigour model would present avenues for new content, training programmes, and a potential HE-industry manifesto. This MdMc model may offer a quick and dynamic process of epistemic, accessibility and instructional rigour checks to achieve professional currency towards self-sufficiency for IEs.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

Active and transformative learning is a holistic strategy for the enhancement of the unique value proposition of education and learning. In Chapter 1 of this edited volume we introduced a unique value proposition on the impact of ATL in higher education and we communicated a five-tier value framework including pillars related to ATL, higher education, industry, entrepreneurship, research& development, and innovation. In this concluding chapter we move the discussion on another context. With emphasis on the robust and resilient strategy of the Vision 2030 in the Kingdom of Saudi Arabia we comment on the capacity of the ATL to facilitate, to enable and to transform positively the added value of the Vision 2030 implementation. The chapter is organized as follows. At the beginning, we provide a short overview of the Vision 2030 mandate, and then based on the ATL metaphors we presented in Chapter 1 of this edited volume, we strategize the ATL framework towards the implementation and the resilience of the Vision 2030.

Active and transformative learning (ATL) challenges the new era of teaching and learning in higher education. In this chapter a systematic approach to ATL theories and methodologies is provided. Authors provide a unique methodological framework for the integration of active learning practices in modern higher education curricula. This chapter also provides an integrated context for the overall discussion of the phenomenon in the volume. The recent arrivals of new disruptive artificial intelligence-enabled technologies such as OpenAI, ChatGPT, DeepAI and others are only few aspects of a new challenging era for the teaching, learning, innovation and sustainability in the higher education.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.