Search Results (1,519)

This study examines the interplay among ICT skills, perceptions of employability, and entrepreneurial intention among university students, focusing on how generic and scarce ICT competencies influence their confidence in employment opportunities and their inclination toward entrepreneurial intentions. Drawing on the Theory of Planned Behaviour, the study explores how digital competencies shape entrepreneurial attitudes, perceived feasibility, and behavioural readiness. A quantitative research approach was adopted, and data were collected using a convenience sampling method from 117 university students enrolled in ICT-related programmes. A reliability analysis, exploratory factor analysis, correlation analysis, regression analysis, and chi-square tests were used to examine the relationships among ICT skills, employability perceptions, and entrepreneurial constructs. Findings reveal that students possess strong generic ICT skills and high self-efficacy, suggesting confidence in their general capabilities and labour market readiness. However, scarce ICT skills were found to be unevenly distributed across departments and campuses, indicating disparities in access to advanced technical training. Regression results show that both generic ICT skills (β = 0.27, p < 0.01) and scarce ICT skills (β = 0.34, p < 0.001) significantly predict employability (R² = 0.29), while generic (β = 0.29, p < 0.01) and scarce ICT skills (β = 0.46, p < 0.001) significantly influence perceived feasibility (R² = 0.41). Furthermore, employability (β = 0.31, p < 0.01) and perceived feasibility (β = 0.25, p < 0.05) significantly predict entrepreneurial intention (R² = 0.27). The results also show strong entrepreneurial desirability among students, yet perceived feasibility remains comparatively low, highlighting a gap between entrepreneurial aspiration and perceived capability. Importantly, advanced ICT competencies strengthen students’ confidence in their ability to pursue entrepreneurial activities. The study concludes that strengthening scarce ICT competencies, experiential entrepreneurship education, and industry collaboration within higher education institutions is essential for enhancing graduate employability and entrepreneurial potential in South Africa. Full article

Background: In 2021, an Australian Hospital Nursing Research Hub sponsored 13 healthcare staff to complete the Graduate Certificate (Clinical Redesign), to build capability in health service improvement though work-integrated learning (WIL). Healthcare professionals undertaking workplace-based WIL likely experience significant challenges including balancing professional and student roles and aligning work with academic requirement. These pressures were likely intensified during the Coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore and understand the experiences of hospital healthcare staff completing WIL redesign projects, including the impacts of COVID-19. Methods: A qualitative descriptive inquiry approach was used to explore individual student experiences. Thirteen staff, mostly nurses, who enrolled in the 2021 course were invited to participate. Online semi-structured interviews were conducted. Data were analyzed using a general inductive thematic analysis approach. Results: Four participants (36%) took part; all were female and working full-time. Five main themes were identified that centered around: COVID-19, Support, Motivation, Alignment and Relevance, and Success. Conclusions: Novel insights include the need to reconceptualize “success” to improve student experience, the critical role of organizational–university–student alignment in enabling WIL studies, and the unique pressures of completing WIL during crisis conditions that direct impact the health sector, such as COVID-19. Although not generalizable, these findings are likely to be important considerations more broadly to strengthen WIL design, support and student experiences, ultimately enhancing health service staff capability to lead quality improvement in the workplace. Full article

Many physics educators seek to improve their courses but feel constrained by traditional post-secondary structures and norms. Instructors often perceive a false tension between fostering inclusive learning environments and maintaining the rigor central to the discipline. The Effective Practices for Physics Programs (EP3) Guide synthesizes decades of research-based recommendations for improving physics education. However, it offers limited guidance on how to integrate these diverse recommendations into a coherent, course-level approach—a responsibility that falls to individual instructors, whose graduate training prepared them primarily as researchers rather than as educators. This paper begins by motivating and introducing the Practicing Professionalism Framework (PPF), a course design framework developed in alignment with EP3 recommendations that encourages development of professional skills in a way that connects students’ interests and values to the broader physics community. We present the PPF in sufficient detail to enable motivated faculty to adopt and adapt it as a research-informed tool for aligning their course design with both their professional values and instructional goals. Next we present the PPF implemented in two very different instructional contexts, demonstrating how the PPF can offer a structured pathway for making courses more inclusive while preserving disciplinary rigor. We conclude with observations across the two case studies. Full article

Background: Evidence shows that simulation-based education for nurses and midwives contributes to strengthening patient safety and quality of care in healthcare settings. Nevertheless, it is implemented to a limited degree in Sub-Saharan African (SSA) higher education institutions, including Tanzania. This demands that Tanzania shift from a traditional model of teaching to incorporate simulation-based education to produce a skilled workforce. Objective: To explore perceptions and experiences of nurse educators (lecturers) and midwives on simulation-based education in Tanzania. Methods: The study employed a generic qualitative descriptive study design with purposive sampling. The data were collected through individual semi-structured interview guides with nurse educators and midwives (nine nurse educators and 11 midwife graduates) from two selected universities in the School of Nursing and their respective teaching hospitals. Qualitative inductive content analysis was used to analyze the data. Results: The data analysis revealed three themes and nine sub-themes: 1. Knowledge and skills in simulation-based education. 2. Challenges in the implementation of simulation-based education. 3. Ensuring patients’ safety. Conclusions: Students were indeed experienced, but not trained in how to use simulation-based education, and nurse educators had inadequate skills. A high number of students with inadequate infrastructure and resources is the major challenge experienced by participants. Simulation-based education is at an early stage of adoption in Tanzania and will require ongoing development, support and resources to fulfilll its potential in promoting patient safety. Full article

There are numerous ways in which generative artificial intelligence (GAI) can be applied in the teaching and learning process. This paper presents one application in the Business Decision Analysis (BDA) course. BDA is considered as the most challenging course in the Graduate Study Program in Economic Entrepreneurship at the University of Zagreb Faculty of Organisation and Informatics; consequently, the teachers continuously analyse possibilities to make the course more attractive for students. The innovative teaching activity at BDA was implemented as a betting shop during the first colloquium (which accounts for 50% of the overall grade). In the activity, GAI analysed learning management system (LMS) data of students’ results (attendance, self-assessment test results, logs in the system) of the initial (pre-course) test, as well as their results of the pub quiz (activity organised a week before the colloquium as a preparatory activity). GAI analysed all the data and predicted the number of points each student will achieve. Additionally, GAI calculated the risk index, average growth (among self-assessment tests) and learning consistency for each student. Finally, GAI created a message for each student that explained what went well in their learning activity, what could be improved, and included a motivational note for the test. The rule was: if a student achieved a higher result than the GAI predicted, the teacher would buy a chocolate for that student. More than 60% percent of students achieved a higher score than was predicted. Surprisingly, exceeding the expected result was not in correlation with the risk indices determined by the GAI. Cluster analysis identified four student profiles consistent with the correlation results, showing weak overall agreement between the predicted and achieved scores, except in the male subgroup, while higher predicted scores were associated with higher average growth and lower risk indices. Qualitative analysis of the GAI application in teaching yielded positive comments, as students perceived the activity as helpful, motivating, and engaging, and would have liked more similar activities. Full article

The Individuals with Disabilities Education Act (IDEA) provides special education services to students with disabilities, including autistic students, until age 21. However, the ages at which autistic students exit high school—and the reasons for exit—are not well documented, despite their importance for transition planning. We analyzed U.S. Department of Education Section 618 Part B data for special education students ages 14–21 across five school years (2014–2015 to 2018–2019) to examine exit age and exit category, with comparisons among autistic students, students with intellectual disabilities (IDs), and students with other disabilities. Using publicly reported counts of students exiting at each age, we derived mean exit ages by transforming age-specific count data. In 2019, 71% of autistic students graduated with a diploma, compared with 48% of students with IDs and 72.5% of students with other disabilities. Autistic students had lower dropout rates (6–8%) than students with other disabilities (15–18%). The mean exit age for autistic students was approximately 18 years, with an average graduation age of 17.9 years, indicating that many students exited prior to the end of extended IDEA eligibility in their state. These findings provide descriptive context on when autistic students exit high school relative to IDEA eligibility and underscore the importance of transition planning and coordination with adult service systems, though these factors were not directly examined in the present analysis. Full article

Background/Objectives: Low back pain (LBP) is one of the most common musculoskeletal conditions globally and a leading cause of disability. University populations may be particularly vulnerable due to prolonged sitting, academic stress, and frequently suboptimal ergonomics, especially in rapidly expanding higher education systems such as those in Saudi Arabia. This systematic review and meta-analysis aimed to synthesize evidence on the prevalence of LBP among university attendants in Saudi Arabia and to quantify its associations with key demographic and environmental risk factors. Methods: We systematically reviewed observational studies reporting LBP prevalence and/or risk factors among university students and faculty in Saudi Arabia published in English, following Cochrane methodological guidance and PRISMA 2020 reporting recommendations. The protocol was prospectively registered in PROSPERO (CRD420250654048). We searched PubMed, Embase and CINAHL from inception to February 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Joanna Briggs Institute checklist for analytical cross-sectional studies. Random effects meta-analyses were used to pool prevalence estimates across recall periods, regions, populations, and measurement tools, and to calculate pooled odds ratios (ORs) for age, sex, smoking, family history of LBP, and college seating conditions. Heterogeneity, subgroup, and sensitivity analyses were undertaken. Results: Thirteen cross-sectional studies were included. The overall pooled prevalence of LBP was 57% (95% confidence interval [CI] approximately 43–71), with substantial heterogeneity. Prevalence varied by recall period, region, population group, and measurement instrument; pooled prevalence was 58% among students and 50% among faculty. Increasing age (OR 1.17, 95% CI 1.01–1.34) and poor college seating conditions (OR 1.42, 95% CI 1.07–1.76) were significantly associated with LBP. Male gender, smoking, and family history showed non-significant pooled effects. These estimates are limited by substantial between-study heterogeneity, variable measurement tools, and exclusively cross-sectional designs, which restrict causal inference. Conclusions: LBP is prevalent among university attendants in Saudi Arabia, affecting both students and faculty. The consistent associations with age and seating ergonomics highlight the need for ergonomic classroom redesign and age-sensitive preventive strategies. Future work should adopt standardized LBP measures and longitudinal designs to clarify causal pathways and evaluate targeted interventions. Funding: This work was supported by the Deanship of Graduate Studies and Scientific Research at Jouf University (grant DGSSR-2026-NF-01-002). Full article

Generative artificial intelligence in higher education is typically framed as either a student productivity tool or an institutional disruption. This agenda-setting essay advances a third position: mobile generative AI functions as relational infrastructure—a persistent communicative presence that mediates identity, meaning-making, and belonging during institutional transition. Focusing on international graduate student onboarding, we abductively “think through” two complementary theoretical lenses. Constitutive Artificial Intelligence Identity Theory (CAIIT) conceptualizes AI as a co-constitutive participant in identity formation through recursive communicative feedback loops. Language Convergence/Meaning Divergence (LC/MD) theory explains how shared institutional language masks interpretive gaps across intercultural and bureaucratic contexts. Reading narrative vignettes through these frameworks, we argue that generative AI is neither simple curricular tool nor personal aid, but both relational and organizational infrastructure, redistributing translational, emotional, and interpretive labor in higher education. We outline four design principles for AI-integrated onboarding: distinguish communicative scaffolding from cognitive replacement; design systems that assume meaning divergence; center equity in AI-mediated transitions; and anticipate ethical risk. Reframing AI as relational infrastructure shifts AI-in-education research toward relational accountability and institutional care. Full article

Teaching biblical history and biblical archaeology to undergraduates presents distinctive pedagogical challenges. Unlike graduate students, undergraduates often enroll with limited historical literacy, minimal exposure to ancient Near Eastern history, and religiously shaped assumptions about the Bible that have not been examined critically. At the same time, the cursory treatment of the biblical world in standard Western Civilization textbooks leaves many students without adequate chronological and historical frameworks. Presenting undergraduates with the complex historiographic issues innate to the field is problematic, as it can lead to alienation or even challenges to faith. This essay argues that instructors must be clear about their approaches and keep the distinction between teaching religion and teaching about the Bible as a historical document explicit, while acknowledging the diverse backgrounds with which students enter the classroom. The article uses several examples (including approaches to the Exodus narrative) to demonstrate how scholarship can be presented responsibly. The essay also addresses disciplinary and terminological complications. Full article

Additive manufacturing in construction is expanding production possibilities for housing, however its integration into architectural design workflows remains limited. This research proposes a computational workflow for the early-stage form-finding of housing volumes intended for additive construction. A parametric design system was developed to generate a wide range of residential volumetric configurations based on geometric parameters derived from conventional housing typologies and emerging 3D-printed construction practices. The design space was explored through user-driven experimentation and automated evolutionary optimization targeting predefined surface area conditions. Besides design alternatives were visualized using AI-assisted image generation to support comparative evaluation, translated into BIM models for further architectural development, and tested through physical 3D-printed scale models to assess material expression and constructability. Five design exploration activities involving architects and graduate students produced nearly 200 volumetric alternatives, in order to review its use and possibilities. The results show that the parametric system enables efficient exploration of both conventional and novel housing forms potentially compatible with additive construction. Vertically articulated volumes with curved envelopes and spatial variation emerged as promising alternatives. The study demonstrates the potential of integrating parametric modeling, evolutionary search, AI-assisted visualization, and physical prototyping to support architectural decision-making and facilitate the incorporation of 3D printing into housing design processes. Full article

The rapid development of Generative Artificial Intelligence (GenAI) in graduate education has changed human–AI interaction within knowledge-intensive environments, leading to important questions about user-side cognitive adaptation in probabilistic AI systems. While many studies focus on ethical implications, limited attention has been paid to the cognitive mechanisms underlying graduate students’ adoption of GenAI. Drawing on the Technology Acceptance Model (TAM), this study explores the cognitive and interactional mechanisms shaping graduate students’ adoption and usage of GenAI. Using thematic analysis of in-depth interviews with 20 graduate students from diverse academic backgrounds, the study identifies seven interrelated constructs: perceived usefulness, perceived ease of use, external environment, risk perception, attitude, behavioral intention, and interaction subjectivity. This study demonstrates that the adoption of GenAI is not merely a result of perceived efficiency but is shaped by cognitive calibration between trust and risk evaluation. Moreover, interaction subjectivity emerges as a metacognitive factor that determines whether engagement results in human–AI collaboration or passive automation. By integrating external environment, risk perception, and interaction subjectivity, this study provides a cognitively grounded framework for understanding human–AI adoption and interaction dynamics. Practically, the findings provide design-relevant insights for developing GenAI systems that support calibrated trust, uncertainty awareness, and adaptive cognitive participation. Full article

Background/Objectives: Breast cancer is one of the most prevalent cancers among women, with notable increases among women younger than 50 years. Knowledge about breast cancer and preventive measures (e.g., early detection) are key to reducing breast cancer morbidity and mortality. Many graduate students fall within an age range when breast cancer risk starts to rise. However, research focused specifically on graduate students’ breast cancer knowledge and practices of preventive measures are sparse. Methods: This scoping review aims to synthesize the literature on breast cancer knowledge and practice of preventive measures among graduate students in a global context. Four databases (PubMed, CINAHL, APA PsycINFO, Embase) were searched for articles published between 2014 and 2024, and the following inclusion criteria were applied: full-text peer-reviewed articles available online; target population includes graduate students aged 20 to 50 years; English language; and, cross-sectional, cohort, case–control, ecological, and experimental/intervention studies. Two reviewers independently conducted article screening and data extraction using Covidence. Results: Sixteen studies met the selection criteria. Knowledge of breast cancer was examined in 94% (15/16) of the included studies and 75% (12/16) of the studies assessed practice of prevention measures (e.g., breast self-examination) among graduate students. Overall, most studies reported poor knowledge and limited uptake of prevention practices. Educational background (e.g., years of education, academic discipline and GPA), access to healthcare services, and other socioeconomic characteristics were commonly reported factors significantly associated with breast cancer knowledge and practice of preventive measures in graduate students. Conclusions: These findings may inform targeted educational interventions to increase knowledge and promote the early detection and prevention of breast cancer among graduate students. Full article

Most state-of-the-art machine learning techniques revolve around the optimization of loss functions, making the choice of an objective critical to model performance and reliability. Although recent reviews discuss loss functions in specific domains or in deep learning settings, there is still no single reference that presents widely used losses across major task families within a unified formal setting and with consistent optimization-relevant property annotations. In this survey, we compile and systematize the most widely adopted loss functions for regression, classification, generative modeling, ranking, energy-based modeling, and relational learning. Our selection procedure combines seeding from foundational textbooks and prior surveys with cross-checking of highly cited literature and common implementations in mainstream machine learning frameworks. We introduce 52 loss functions and organize them into an intuitive taxonomy, summarizing their theoretical motivation, key mathematical properties, and typical application contexts, with compact appendix tables for quick lookup. This survey is intended as a resource for undergraduate, graduate, and Ph.D. students, as well as researchers seeking a structured reference for selecting and comparing loss functions. Full article

The All of Us Researcher Academy Internship Program provided a 3-month experiential learning opportunity for graduate and undergraduate students to train on analyzing health data from the All of Us Research Program. Thirteen interns were paired with mentors who have ongoing projects using the All of Us Researcher Workbench, a cloud-based data analysis platform. Interns also participated in networking activities, attended weekly internship supervisor and mentor meetings, and had access to virtual courses. The internship concluded with virtual presentations to share project results. Topics studied included sickle cell disease, cancer, diabetes, sleep disorders, allergic conditions, cardiovascular health, mental health, and healthcare access. The purpose of this evaluation study was to assess the All of Us Researcher Academy Internship Program’s impact on student outcomes during the first two cohorts (2023 and 2024). The study employed a post-only evaluation design. Ten interns completed post-internship surveys that inquired about their overall internship experience, Researcher Workbench use, and research skills development. The 2024 cohort also participated in a focus group discussion that probed their perceptions about the internship experience. Evaluation results revealed that 90% of interns strongly agreed that their overall research skills and self-efficacy improved, and 80% of interns reported interest in future use of the Researcher Workbench. Interns offered positive feedback on their mentorship experiences and reported a strong sense of support and belonging. The All of Us Researcher Academy Internship Program offers an effective model for skills-based experiential learning in biomedical research. Full article

Community pharmacies in the United States (US) face an increasingly unsustainable future due to declining third-party reimbursement (remuneration) and ongoing cash flow challenges following the elimination of retroactive direct and indirect remuneration (DIR) fees. These pressures have contributed to widespread pharmacy closures, the emergence of pharmacy deserts, and reduced access to care for millions of patients. Despite these challenges, community pharmacy remains the most common employment setting for pharmacy school graduates in the US. However, currently required community pharmacy Advanced Pharmacy Practice Experience (APPE) student rotations may offer limited exposure to business, management, and entrepreneurial activities, potentially leaving students underprepared for practice in this setting. US colleges and schools of pharmacy are uniquely positioned to address this gap by partnering with their community pharmacy APPE rotation sites to intentionally integrate business- and practice-focused knowledge, skills, and attitudes (KSAs) into the APPE. Equipping students with these KSAs may enhance early career readiness while also supporting the financial sustainability of US community pharmacies through the development of innovative, revenue-generating services. These efforts further align with the 2025 Accreditation Council for Pharmacy Education (ACPE) Standards and may help advance the profession. Future research should examine optimal community pharmacy APPE structures, models, and assessment strategies to maximize student preparedness and long-term community pharmacy sustainability. Full article