Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

Primary school students are in a period of rapid development of thinking. Primary school mathematics is particularly important for the cultivation of students' abstract thinking ability. The section of number and algebra is the most basic and important content in mathematics. This paper takes number and algebra as an example to analyze the abstract thinking ability of primary school mathematics and its training strategies, so as to provide some practical guidance for teaching.

Arabic rhetoric has traditionally relied on ancient texts and human interpretation for teaching purposes. The study investigates ChatGPT’s ability to analyze and interpret Arabic rhetorical devices, specifically examining its capacity to handle cultural and contextual elements in rhetorical analysis. Drawing on institutional implementation frameworks and recent educational technology research, this study examines policy considerations for Arabic rhetoric education in an AI-driven environment, with a particular focus on sustainable digital infrastructure development and systematic reforms needed to support AI integration. The study employed the comparative approach to analyze eight rhetorical examples, including metaphors (“Zaid is a lion”), similes (“Someone is a sea”), and metonymy (“A person full of ash”), then compare ChatGPT’s interpretations with traditional explanations from classical Arabic rhetoric texts, particularly “Dala’il al-I’jaaz” by al-Jurjani. The results demonstrate that ChatGPT can provide basic interpretations of simple rhetorical devices, but it struggles with understanding cultural contexts and multiple layers of meaning inherent in Arabic rhetoric. The findings indicate that AI tools, despite their potential for modernizing rhetoric education, currently serve best as supplementary teaching aids rather than replacements for traditional interpretative methods in Arabic rhetoric instruction.

Social Prescribing (SP) is an approach which aims of improving health and well-being and connecting patients to community services. Examples of these services include physical activity and cultural activities. Despite its benefits, SP has still not been fully implemented in Portugal. This case study is part of a larger study on Social Prescribing Local System (SPLS) implementation, which comprised a quantitative approach, a pilot study and a qualitative approach, and aims at exploring patients’ and healthcare workers’ perspectives on SP. The study was carried out to understand the motivations of different stakeholders for participating in the pilot project, the anticipated benefits for patients, healthcare professionals, and the health unit, as well as their perceptions and experiences within the scope of the SP project. Data collection was carried out in December 2020 through semi-structured individual interviews and a focus group. A total of seven participants were included, of which one patient, one museum representative and five healthcare professionals. Different common dimensions related to SP emerge, including health and well-being, social interaction and community engagement, accessibility and inclusivity, motivation and adherence, collaboration and coordination, and education and awareness. The patient considered the adequacy of the activity to the patient’s state of health and capabilities, adoption of a phased approach, with a focus on progress, in order to promote long-term adherence as facilitators. For the museum, disseminating its activities to healthcare professionals and patients through different channels such as posters at the health center, social media pages, and training sessions can significantly enhance visibility and engagement, while direct phone contact and digital publications can further promote adherence, ensuring a comprehensive and coordinated approach to patient participation and institutional benefit. Healthcare professionals identified several benefits, including reduction of social isolation and sedentarism, as well as a means of strengthening the therapeutic relationship with patients. The design and implementation of SP programs should be participative and involve all stakeholders participating in the process. Barriers to adherence included time for activity and the associated costs or prerequisites, availability of activities and lack of perceived interest in health.

In learning, one of the fundamental motivating factors is self-efficacy. Therefore, it is crucial to understand the level of students’ self-efficacy in learning programming. This article presents a quantitative study on undergraduate students’ perceived programming self-efficacy. 110 undergraduate computing students took part in this survey to assess programming self-efficacy. Before being given to the respondents, the survey instrument, which included a 28-item self-efficacy assessment and 30 multiple-choice programming questions, was pilot-tested. The survey instrument had a reliability of 0.755. The study results show that the students’ self-efficacy was low when they solved complex programming tasks independently. However, they felt confident when there was an assistant to guide them through the tasks. From this study, it could be concluded that self-efficacy is an essential achievement component in programming courses and can avoid education dropouts.