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Prior research finds that early-career professional accountants (early PAs) are generally dissatisfied with the learning and training opportunities offered during their early employment years, which impacts their career progression. This paper aims to examine whether different learning styles between these early PAs and their qualified accounting employers and trainers diverge.

Using Kolb’s learning styles inventory and interviews, this study explores the learning styles of early PAs and their employers, examining changes in these learning styles over time.

This research shows the necessity for different learning styles to be integrated into early PA training and learning, as research participants’ learning styles tend to prefer active experimentation requiring practical examples and self-learning opportunities. In contrast, their senior, qualified accounting employers prefer conceptualisation-based learning styles. As early PAs’ career progression requires them to succeed in employer-supported training, some early PAs change their learning style preferences to progress, whereas others with incompatible learning styles either moved to different employers or reassessed their choice of profession.

To reduce career dissatisfaction, develop and retain competent accountants, early PAs must be supported to learn effectively. By reducing early PAs’ dissatisfaction early PA educators and employers will potentially increase the attractiveness of accounting as a profession.

Few studies interrogate how accounting professionals utilise learning and training post-graduation nor do they examine the learning styles of workplace trainers and learners. This exploratory study uniquely analyses the learning styles of both early PAs and their employers.

Leadership is key to building a culture of continuous learning within organizations. This study aims to explore the pivotal role of leadership in creating a culture of constant learning within organizations by bibliometric and content analysis. It also introduces propositions and frameworks that emphasize the importance of fostering a growth mindset, encouraging knowledge sharing, promoting learning agility and leveraging technology to support lifelong development.

Using data from the Web of Science Core Collection, the author performed a complete analysis of publication features, collaboration networks and keywords in the field using VOSviewer software. Furthermore, drawing on social cognitive theory, this paper also presents propositions that integrate key concepts and strategies for fostering a culture of learning.

The data shows a large increase in publications on leadership and organizational learning, particularly since 2000, indicating an increasing interest and importance in this field. The author proposes leaders who demonstrate a commitment to continuous improvement and invest in learning resources empower their teams to embrace new challenges and explore innovative solutions. By fostering a culture of learning, organizations can enhance employee engagement, foster creativity and innovation and adapt more effectively to changing market dynamics.

This study offers a unique perspective on the role of leadership in driving learning and development initiatives. By implementing the principles, organizations can create a competitive advantage by cultivating a workforce that is agile, resilient and equipped to thrive in an ever-changing world.

As internet banking service marketing platforms continue to advance, customers exhibit distinct behaviors. Given the extensive array of options and minimal barriers to switching to competitors, the concept of customer churn behavior has emerged as a subject of considerable debate. This study aims to delineate the scope of feature optimization methods for elucidating customer churn behavior within the context of internet banking service marketing. To achieve this goal, the author aims to predict the attrition and migration of customers who use internet banking services using tree-based classifiers.

The author used various feature optimization methods in tree-based classifiers to predict customer churn behavior using transaction data from customers who use internet banking services. First, the authors conducted feature reduction to eliminate ineffective features and project the data set onto a lower-dimensional space. Next, the author used Recursive Feature Elimination with Cross-Validation (RFECV) to extract the most practical features. Then, the author applied feature importance to assign a score to each input feature. Following this, the author selected C5.0 Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost and LightGBM as the six tree-based classifier structures.

This study acclaimed that transaction data is a reliable resource for elucidating customer churn behavior within the context of internet banking service marketing. Experimental findings highlight the operational benefits and enhanced customer retention afforded by implementing feature optimization and leveraging a variety of tree-based classifiers. The results indicate the significance of feature reduction, feature selection and feature importance as the three feature optimization methods in comprehending customer churn prediction. This study demonstrated that feature optimization can improve this prediction by increasing the accuracy and precision of tree-based classifiers and decreasing their error rates.

This research aims to enhance the understanding of customer behavior on internet banking service platforms by predicting churn intentions. This study demonstrates how feature optimization methods influence customer churn prediction performance. This approach included feature reduction, feature selection and assessing feature importance to optimize transaction data analysis. Additionally, the author performed feature optimization within tree-based classifiers to improve performance. The novelty of this approach lies in combining feature optimization methods with tree-based classifiers to effectively capture and articulate customer churn experience in internet banking service marketing.

Visually impaired people usually struggle with doing daily tasks due to a lack of visual cues. For image captioning assistive applications, most applications require an Internet connection for the image captioning generation function to work properly. In this study, we developed MyUEVision, an application that assists visually impaired people by generating image captions that can work with and without the Internet. This work also involves reviewing some image captioning models for this application.

The author has selected and experimented with three image captioning models for online models and two image captioning models for offline models. The user experience (UX) design was designed based on the problems faced by visually impaired users when using mobile applications. The application is developed for the Android platform, and the offline model is integrated into the application for the image captioning generation function to work offline.

After conducting experiments for selecting online and offline models, ExpansionNet V2 is chosen for the online model and VGG16 + long short-term memory (LSTM) is chosen for the offline model. The application is then developed and assessed, and the results show that the application can generate image captions with or without the Internet, providing the best result when having an Internet connection, and the image is captured in good lighting with a few objects.

MyUEVision stands out for its both online and offline functionality. This approach ensures the image captioning generator works with or without the Internet, setting it apart as a unique solution to address the needs of visually impaired individuals.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Online learning has come to stay in a technologically advancing world with increasing populations. The search for ways to make online learning more efficient and effective in some developing countries continues as the accompanying issues in developing country contexts abound. This paper explores the issues that underlie online learning management in a developing country context, focusing on the Moodle, Sakai and Zoom platforms used in some selected public universities in Ghana.

The study relied on the qualitative approach to data collection and a descriptive design for analysis. Using the social constructivism theory, the paper discussed the critical issues students, and to a lesser extent, instructors encounter in their engagement on these platforms for academic learning.

The study found that the inefficient use of these platforms is due to several factors including the high cost of data, poor perception of the quality of online learning compared to traditional in-person contacts, poor attitude of students, low participation in online learning, lack of computers and poor internet connectivity among others.

It is important to mention that this study was limited to some selected public universities in Ghana. Data could have been collected from a wider sample including other Ghanaian tertiary institutions or some other developing countries. The similarity of study outcomes in other developing countries, however, suggests that similar results would have been obtained in an international sample.

Despite the robustness of the Learning Management systems in place, the evidence suggests that their utilisation is far less than optimal. However, with relevant policies and the provision of needed technical support, training, provision of equipment like computers for use by both students and instructors and efficient internet connectivity, the LMS platforms could be more efficient for online learning.

The authors conducted this research using original data from interviews in the selected public universities in Ghana. The data was meant to inform the discussion on some of the critical issues that underlie online learning in a developing country context. Although the study relied on data from selected public universities in a single country, its outcomes reflect fundamental issues of online learning in a developing country context which find relevance in available study outcomes.

This study compared approaches to school closures in four Latin American countries (Bolivia, Colombia, Ecuador, Peru), describing the impact on the health and educational wellbeing of school-age children and youth, and evaluating their approaches in regard to continuing education through the pandemic.

We collected 75 publicly available documents including scientific and gray literature (government documents and news releases), that referred to school closures and their impact on children’s health and wellbeing. We did thematic analyses using open, axial, and selective coding and applied the latest Health Promoting Schools standards and indicators to the findings.

Results showed that countries followed epidemiological reasons for prioritizing school closures while adopting some policies that abide by Health Promoting School principles. While they emphasized the need to reopen schools so that instruction could continue, school closures were among the longest in the world. The most significant impacts on wellbeing identified in the four countries were related to food security and mental health.

This study focused on a particular set of documents, and it may not capture the full spectrum of relevant information in different contexts or regions.

By comparing school closures approaches among four Latin American countries, this study highlights the importance of context-specific interventions. In a post-pandemic era, lessons learned from these experiences should help foster more resilient and inclusive educational systems and explore the paths forward for following the new Health Promoting Schools framework in the region.

Cross-country qualitative analyses on this topic are rare. This study adds to the knowledge base by eliciting lessons for future health education research and policy efforts.

This paper aims to investigate how manufacturers can foster insights and improvements from real-time data among shop-floor workers by developing organisational “learning-to-learn” capabilities based on both the lean- and action learning principle of learning through problem-solving. Second, the purpose is to extrapolate findings on how action learning can enable the complementarity between lean and industry 4.0.

An insider action research approach is adopted to investigate how manufacturers can enable their shop-floor workers to foster insights and improvements from real-time data at VELUX.

The findings report that enabling shop-floor workers to use real-time data consist of developing three consecutive organisational building blocks of learning-to-learn, learning-to-learn using real-time data and learning-to-learn generating real-time data − and helping others to learn (to learn).

First, the study contributes to theory and practice by demonstrating that a learning-to-learn capability is a core construct for manufacturers seeking to enable shop-floor workers to use real-time data-capturing systems to drive improvement. Second, the study outlines how lean and industry 4.0 complementarity can be enabled by action learning. Moreover, the study allows us to deduce six necessary conditions for enabling shop-floor workers to foster insights and improvements from real-time data.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

This study aims to explain the experiences of nursing students while they practiced gerontological nursing through online media during the COVID-19 pandemic.

Purposive sampling was used to select 20 third-year nursing students. Semi-structured face-to-face interviews were conducted with participants. The collected data were analyzed using the thematic analysis method to identify recurring themes and patterns.

Four themes emerged from the interviews, including improved practical skills, the impact of online learning, receiving support from close individuals and the need for support. Participants provided specific examples of how they improved their skills through online learning and described the importance of receiving support from those close to them during this challenging time.

Online media is essential for teaching nursing students during the COVID-19 pandemic. This is the main reason for teaching nursing students during times of restricted access to clinical settings. This research provides insights into the challenges and benefits associated with using digital platforms to teach gerontological practice for nursing students.