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This paper examines the financial challenges of single mothers in Brunei, illustrating how single parenting affects their lifestyle choices and the coping mechanisms these mothers employ to grapple with their financial hardships.

This study adopts a qualitative approach, utilizing semi-structured interviews with five single mothers living in the Brunei-Muara district. The collected data were analyzed using thematic analysis.

Results revealed participants described operating with a single income, covering bills alone, the high cost of living in Brunei and the absence of alimony contributed to their financial challenges. Financial stress caused them to change their spending habits, switch to cheaper alternatives, make sacrifices and struggle to meet the needs of their children. Coping mechanisms adopted by single mothers utilized social support, generating side income, and the unique roles of children played a significant role in cushioning the impact of their financial difficulties.

Single mothers are on the rise in Brunei. There is an acute lack of studies emphasizing the financial hardships experienced by single mothers in Brunei. This study attempts to narrow the missing gap and seek avenues of justice for single mothers in a progressive, equitable society.

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.

The purpose of this study is to investigate and analyse the potential of existing buildings in the UK to contribute to the net-zero emissions target. Specifically, it aims to address the significant emissions from building fabrics which pose a threat to achieving these targets if not properly addressed.

The study, based on a literature review and ten (10) case studies, explored five investigative approaches for evaluating building fabric: thermal imaging, in situ U-value testing, airtightness testing, energy assessment and condensation risk analysis. Cross-case analysis was used to evaluate both case studies using each approach. These methodologies were pivotal in assessing buildings’ existing condition and energy consumption and contributing to the UK’s net-zero ambitions.

Findings reveal that incorporating the earlier approaches into the building fabric showed great benefits. Significant temperature regulation issues were identified, energy consumption decreased by 15% after improvements, poor insulation and artistry quality affected the U-values of buildings. Implementing retrofits such as solar panels, air vents, insulation, heat recovery and air-sourced heat pumps significantly improved thermal performance while reducing energy consumption. Pulse technology proved effective in measuring airtightness, even in extremely airtight houses, and high airflow and moisture management were essential in preserving historic building fabric.

The research stresses the need to understand investigative approaches’ strengths, limitations and synergies for cost-effective energy performance strategies. It emphasizes the urgency of eliminating carbon dioxide (CO₂) and greenhouse gas emissions to combat global warming and meet the 1.5° C threshold.

The purpose of this paper is to add to the small but growing body of research examining the influence of founder gender on new venture access to venture development programs.

Hypotheses were tested utilizing a sample of 482 nascent technology ventures which applied for admittance into a venture development organization headquartered in the southern region of the United States from March 2004 through February 2016.

Findings suggest that female-founded applicant ventures experience a higher likelihood of acceptance into venture development programs than male-founded applicant ventures. Results further suggest that social attention to gender equality reduces this effect for female-founded applicant ventures. Findings extend the understanding of the gendered nature of high-technology venturing and venture development organizations.

The findings of this study may not generalize to new ventures operating in other contexts (e.g., non-U.S., low-tech, and other venture development programs). Additionally, this study's design and data limitations do not allow for the establishment of causality or address founder motivations to apply for acceptance into venture development programs.

This study adds to empirical findings regarding the influence of founder gender on new venture acceptance into venture development programs by developing and testing competing hypotheses. This study also extends extant research by examining the moderating effect of social attention to gender equality on the hypothesized relationships between founder gender and acceptance into venture development programs.

This paper reviews the extant research on Web3.0 published between 2003 and 2022.

This study uses a topic modeling procedure latent Dirichlet allocation to uncover the research themes and the key phrases associated with each theme.

This study uncovers seven research themes that have been featured in the existing research. In particular, the study highlights the interaction among the research themes that contribute to the understanding of a number of solutions, applications and use cases, such as metaverse and non-fungible tokens.

Despite the relatively small data size of the study, the results remain significant as they contribute to a more profound comprehension of the relevant field and offer guidance for future research directions. The previous analysis revealed that the current Web3.0 technology is still encountering several challenges. Building upon the pioneering research in the field of blockchain, decentralized networks, smart contracts and algorithms, the study proposes an exploratory agenda for future research from an ecosystem approach, targeting to enhance the current state of affairs.

Although topics around Web3.0 have been discussed intensively among the crypto community and technological enthusiasts, there is limited research that provides a comprehensive description of all the related issues and an in-depth analysis of their real-world implications from an ecosystem perspective.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

This study aims to capture children’s interpretation of holistic well-being within Irish primary schools and add to the development of a comprehensive systems-informed positive education model.

This study utilized visual participatory research methods, including PhotoVoice and one-on-one interviews, to assess children’s (n = 16) well-being, guided by Von Unger’s comprehensive seven-step framework. Data analysis was anchored within grounded theory, beginning with data collection, initial coding, focused coding and culminating in identifying themes and subthemes. Data were interpreted using the mosaic approach by integrating visual and verbal data.

This analysis uncovered three primary themes that affect student well-being: relationships, space and physical environment and learning and curriculum, each with detailed subthemes. For instance, student–teacher relationships, peer relationships, safety, learning spaces, the creative curriculum including arts and music and the experiential richness of outdoor learning are crucial to students' educational growth and well-being. These aspects are seen as interconnected, shaping a holistic educational experience beyond academic learning to encompass students’ comprehensive well-being. The students' narratives demonstrated that learning is not merely an academic exercise but a vital component of their well-being.

This study significantly departs from traditional educational research by advocating for a dynamic, action-oriented understanding of “well-being.” It challenges the static, possessive interpretations of well-being and introduces the concept of well-being as a fluid and ever-evolving process. This reconceptualization positions well-being as a complex construct, influenced by an intricate web of relationships, spanning human and non-human interactions, organizational and environmental structures, personal desires, behavioral practices and broader societal and cultural frameworks.

New digital technologies are reshaping the business landscape and accounting work. This paper aims to investigate how incorporating more data and new data analytics (DA) tools impacts the role and use of judgment in financial due diligence (FDD).

The paper reports findings from a field study at a Big Four accounting firm in Sweden (“DealCo”). The primary data includes semi-structured interviews, observations and other meetings. Theoretically, it draws on Dewey’s The Logic of Judgments of Practise and Logic: The Theory of Inquiry and distinguishes between theoretical (what is probably true) and practical judgment (what to do).

In DealCo’s FDD practice, using more data and new DA tools meant that the realm of possibility had expanded significantly. To manage the newfound abundance and to use DA effectively, DealCo’s advisors invoked practical and theoretical judgments in different stages and areas of the data-driven FDD. The paper identifies four critical uses of judgment: Setting priorities and exercising restraint (practical judgment) and forming hypotheses and doing sense checks (theoretical judgment). In these capacities, practical judgment and theoretical judgment were essential in transforming raw data into actionable insights and, in effect, an indeterminate situation into a determinate one.

The study foregrounds the practical dimension of knowledge production for decision-making and contributes to a better understanding of the role, use and importance of accounting professionals’ judgment in a data-driven world.