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Academic coaching (AC) has gained a significant attention to support student success and achievement in higher education, management and psychology. This study aimed to conduct a comprehensive bibliometric analysis of AC literature to identify the top authors, research patterns, hotspots and research topics in the field.

The study utilized a bibliometric analysis of articles published between 1987 and 2023, using descriptive and network analysis methods with tools such as RStudio, Biblioshiny, Excel and VOSviewer. The study also conducted functional, mapping and content analysis, to identify AC literature's key themes and research areas.

The results revealed an increasing interest in AC, with increased publications. However, there are gaps in geographical diversity and authorship. Most studies were conducted in the United States of America and the UK, and were published in education, psychology and coaching journals. Common themes included coaching, professional development, higher education and mentoring. Emerging research areas include: coaching efficacy in education, AC as an online learning support and professional learning communities. More studies are needed in different contexts and with larger sample sizes.

This comprehensive bibliometric analysis adds to the existing literature by presenting a detailed analysis of the field of AC, filling a gap in the current literature. The study's unique contribution is its examination of emerging research areas and themes in AC literature, providing directions for future research. This study is particularly relevant for researchers, practitioners and policymakers interested in understanding AC's state of the art and identifying promising areas for future research.

This study aims to examine the impact of two dimensions of agile work practices (i.e. agile taskwork and agile teamwork) on team efficacy and creativity. Further, it examines the mediating effect of team efficacy in the relationship between two dimensions of agile work practices and team creativity.

The data were collected from 563 professionals working in 290 information technology (IT) companies in India using a self-reporting structured questionnaire. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothesized model.

The results demonstrate that agile taskwork and agile teamwork positively impact team creativity and team efficacy, and team efficacy positively impacts team creativity. Furthermore, team efficacy partially mediates the impact of agile taskwork and agile teamwork on team creativity.

This study shows the importance of agile work practices and team efficacy to enhance team creativity. The research offers managers strategies to boost team creativity.

There is a dearth of research examining the distinct effects of agile taskwork and agile teamwork on team efficacy and team creativity. Also, this study is one of its kind that examines the mediating mechanisms that explain the effect of agile taskwork and agile teamwork on team creativity.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

The integration of ESD is a complex problem. It calls for an innovative, student-centred curriculum, as well as professional learning and agency, by which university teachers feel empowered to change their practice and direct their peers and institutions towards ESD. This study aims to explore what university teachers consider to be the most important attitudes in supporting their agency to deliver Education for Sustainable Development (ESD) via a Problem Based Learning (PBL) programme.

This study presents a theoretical framework for professional agency comprising three domains: intrapersonal, action and environmental. A Q methodology is adopted to explore university teachers’ perceptions of the most important environmental factors in supporting their ability to deliver ESD via a problem-based learning (PBL) programme. Twenty-eight participants from six Southeast Asian universities took part in a PBL-based professional development programme designed to improve teachers’ ESD- and PBL-based skills and competencies.

The results indicate that the participants were confident in their ability to implement PBL and saw PBL as an approach suitable for addressing current educational, professional and societal challenges. This study offers a series of recommendations to help university teachers develop their ESD and PBL practices.

Although the literature on human agency is extensive, research surrounding teachers’ professional agency in the context of ESD and PBL in higher education is lacking. The present study addresses this gap by capturing individual teachers’ beliefs, perceptions and views and by using Q methodology to examine the subjectivity of study participants.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies.

The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education.

The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies.

The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.

Organisations are investing in systems such as product lifecycle management (PLM) to support product development, collaboration across complex supply chains and to provide a framework for digital transformation. Graduates of apparel programmes would benefit from a knowledge of PLM to help realise the opportunities that PLM offers. The purpose of this paper is to report on an educational research project that used PLM as a context for practice-based learning and as a mechanism to update the learning experience and stimulate the development of future practice.

This paper reports on the experiences, critical reflections and data from an action research study to establish a learning community through an educational partnership for PLM software within an undergraduate fashion business course. The cohort of the first year of the intervention (n = 28) is the main study population.

The findings indicate that PLM provided a stimulating learning context supportive of a detailed understanding of current industry practice, critical and innovative thinking and the development of a professional identity.

The opportunity for the development of both industry and educational practice is outlined.

A general introduction to PLM provides important information to support and advance Fashion Industry 4.0. Educational partnerships can reduce barriers to the integration of advanced technologies into the higher education curriculum.

Applications of PLM are under researched in textiles and apparel. The paper contributes to the broadening of the knowledge base of PLM and its potential to achieve strategic transformation of the sector.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.

The purpose of this qualitative study is to investigate/review the skills required for library and information science (LIS) professionals in the 21st century and to propose an alternative approach as the suggested key skills.

Twenty-two LIS professionals from Pakistan were interviewed, and 10 LIS professionals were from abroad, including two from the USA; six respondents were from Saudi Arabia; one from Canada; and one from Malaysia. In-depth interviews with faculty members were conducted to ascertain their perceptions of the knowledge and skills necessary to be competent in delivering quality education to the future information breed.

The findings emphasise the importance of a variety of competencies for librarians and information educators, including subject knowledge and skills; information technology knowledge and skills; instructional skills; research skills; and managerial, leadership and social skills. Additionally, it was noted that LIS professionals require a diverse set of skills that should be fostered by educators and employers. By promoting these in the broader community, the author can encourage the next generation of LIS professionals to consider LIS as a viable career option.

The findings presented in this paper provide a unique window into the country’s workforce needs. Though the study was conducted from a Pakistani perspective, the findings may have implications for other countries with comparable circumstances, including social impact. It also provides a new analysis of the selected generic and LIS skills that can be communicated in an innovative manner to prospective LIS employees, employers and educators.