The FAANP mentorship program is designed to support professional growth, strengthen confidence, and foster meaningful connections between experienced mentors who are Fellows of AANP and nurse practitioners seeking guidance to develop their skills. Through structured guidance, goal-oriented conversations, and collaborative learning, participants gain personalized support to navigate challenges and refine skills that will help advance their careers. The program promotes a culture of continuous learning, leadership development, and mutual empowerment that benefits individuals, teams, and the broader organization.

Metaverse in healthcare education: A scoping review of definitions and challenges.

Spaces, shaped by everyday practices, also embody a community’s cultural memory. Preserving and transferring this memory to future generations is vital for maintaining urban identity. Within this framework, adaptable reuse provides a sustainable strategy for revitalizing obsolete structures while safeguarding their heritage value. This study examines the adaptive reuse potential of a historical warehouse located within the Konya Train Station Campus, which has received little scholarly attention despite its cultural significance. The research was conducted with fourth-year students enrolled in the Restoration course in the Department of Interior Architecture at Selcuk University. The process followed two stages: (1) a design phase, where students worked in small groups using a collaborative learning approach to propose reuse strategies, and (2) an evaluation phase, where students reflected on the process through a structured survey. Guidance was provided by the instructor through periodic critiques, and student works—including drawings, models, and presentations—were assessed based on contextual sensitivity, creativity, and feasibility. Survey data and qualitative reflections were analyzed to evaluate both the design outcomes and the educational method. Findings reveal that collaborative learning significantly enhanced students’ skills in project management, teamwork, and communication, while also fostering awareness of the social and environmental dimensions of adaptive reuse. The study highlights how integrating adaptive reuse into studio-based education not only strengthens professional competencies but also promotes heritage-sensitive and sustainable design thinking. The conclusions suggest that the approach could be replicated in other educational contexts, offering valuable insights for future curriculum development in interior architecture.

Collaborative deep reinforcement learning algorithm for solving multi-AGV dynamic scheduling problem

Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis

A serial mediation model of the impact of teachers' collaboration on their attitudes: The roles of differentiated instruction and self-efficacy.

Machine learning is transforming the grape and wine industry by shifting traditional experience-driven practices toward data-driven and intelligent decision making across the entire value chain. This review provides a conceptually driven synthesis of machine learning-enabled technologies spanning vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization. An integrated analytical framework is proposed, linking multisource data acquisition, preprocessing and representation learning, model development, and intelligent decision making into a unified end-to-end pipeline. Building on this framework, the review critically examines advances related to five key scientific challenges: multimodal data integration, model interpretability, cross-domain generalization, whole-chain decision coordination, and scalable industrial deployment. Particular emphasis is placed on the mechanisms and trade-offs of multiscale sensing technologies (including spectroscopy, chromatography-mass spectrometry, imaging, and electronic sensing), as well as data preprocessing, feature engineering, and multimodal fusion strategies. A task-oriented and data-structured perspective on model selection is highlighted, covering linear models, kernel methods, ensemble learning, deep neural networks, and probabilistic frameworks, alongside evaluation protocols and interpretability approaches. In contrast to fragmented task-specific studies, this review highlights the importance of cross-stage integration and closed-loop decision systems linking vineyard management with downstream vinification and quality evaluation. Despite rapid progress, key challenges remain, including data scarcity and heterogeneity, limited model transferability, and high implementation costs. Emerging directions such as knowledge-guided machine learning, causal inference, small-sample learning, and human-AI collaboration are expected to enhance robustness and interpretability. Overall, this review provides a structured roadmap for advancing intelligent and sustainable practices in the grape and wine industry.

Tables to teamwork: Student canteen as a catalyst for collaborative learning in business education

HUR-MACL: High-uncertainty region-guided multi-architecture collaborative learning for head and neck multi-organ segmentation

From automation to thinking: The role of AGI in discourse analysis of computer-supported collaborative learning based on computational grounded theory

A systematic review of multimodal learning analytics in computer-supported collaborative learning

Personalized federated learning (PFL) plays a pivotal role in ensuring efficient privacy preservation and secure collaborative learning. However, PFL faces significant challenges due to data heterogeneity and device diversity. To enhance personalization and robustness in PFL, we propose a novel model called FedNODE, which leverages hierarchical embeddings. FedNODE incorporates personalized, pseudo-generic, and fusion embeddings to facilitate hierarchical information representation. We utilize a hypernetwork based on neural ordinary differential equations (ODEs) within the server to generate backbone parameters for different clients, enabling the creation of personalized embeddings. Additionally, we introduce a pseudo-generic embedding based on a learnable vector to balance personalized and generic information. A neural ODE-based network follows the backbone module for each client, integrating personalized and pseudo-generic embeddings. To validate the efficacy of FedNODE, we conduct extensive evaluations across various classification datasets, encompassing diverse statistically heterogeneous settings and noisy scenarios. The results demonstrate that FedNODE achieves state-of-the-art performance.

Teacher appraisal systems in New Zealand have historically emphasised accountability, often at the expense of professional development. This study examines how appraisal processes in two primary schools integrate accountability with developmental goals to support teacher growth. Using a qualitative case study approach, data were collected through semi-structured interviews, participant observations, and document analysis. Thematic analysis revealed that a deeply embedded culture of learning—driven by transformational, pedagogical, and distributive leadership—was central to this integration. School leaders fostered pedagogical improvement through reflective practices, feedback, dialogue, and collaborative inquiry. Mentorship and teaching portfolios emerged as key mechanisms, enabling continuous professional growth and self-assessment while reinforcing accountability. Findings indicate that appraisal systems grounded in collaborative learning, intentional leadership, and reflective practice can move beyond compliance to become powerful tools for teacher development. The study contributes to appraisal reform discourse by demonstrating how schools can reframe accountability as a developmental opportunity, providing practical insights for policymakers, school leaders, and educators seeking to balance evaluation with professional growth.

Science education plays a critical role in developing scientifically literate learners capable of addressing ethical, technological, and societal challenges within rapidly evolving contemporary contexts. This study examined the relationship among science fair engagement, instructional competence, and scientific literacy of Senior High School science teachers in Catholic secondary schools under the Diocese of Iba Educational Foundation, Inc. Employing a concurrent embedded mixed methods research design with a dominant quantitative orientation, data were collected using a validated survey questionnaire measuring multiple domains of science fair engagement, instructional competence, and scientific literacy, while qualitative reflections were embedded to provide explanatory insights supporting statistical outcomes. Fifty-five science teachers participated in the study through total population sampling. Results revealed consistently high levels of science fair engagement, instructional competence, and scientific literacy among respondents. Inferential analyses indicated no statistically significant differences across sex, age, and specialization categories, while highest educational attainment significantly influenced selected domains of instructional competence and scientific literacy. Spearman’s rho correlation analysis demonstrated very strong positive relationships among the three major variables, indicating their mutually reinforcing nature within professional teaching practice. Embedded qualitative findings further explained these relationships by highlighting inquiry driven mentoring, structured instructional scaffolding, collaborative professional learning, faith grounded ethical reflection, and contextual challenges related to time and resource constraints. Grounded in the integrated findings, the study proposes the Cyclical Integrative Model for Faith Integrated Science Education (CIM-FISE), which conceptualizes instructional competence, authentic science fair engagement, and scientific literacy as dynamically interconnected processes sustained through reflective professional growth within Catholic institutional culture. The proposed framework provides an evidence-based guide for strengthening teacher capacity building, inquiry-centered STEM instruction, and ethical scientific formation aligned with global STEM education reforms while preserving the mission of Catholic education in forming scientifically competent, socially responsible, and values-oriented learners.

Medical informatics training consists of two types of fellowship programs: clinical informatics (CI) and imaging informatics (II). Historically, there has been little collaborative learning between these fellowships despite significant overlap in content. This project created a collaborative workshop series for CI and II fellows, which taught mutually-relevant topics and provided an opportunity for cross-disciplinary collaboration in designing workflows for enterprise imaging scenarios. Content was informed by the American Board of Preventive Medicine CI Test Content Outline and an II fellowship curriculum previously published by II fellowship directors. Outputs of the exercise included a Venn diagram displaying similarities and differences between the two specialties, as well as the enterprise imaging workflow designs of each small group. The workshop series was well-received by participating fellows in a follow-up survey, with most (77%) indicating that the content was effective or very effective at showing the need for collaboration between CI and II disciplines and that the knowledge gained would likely be implemented in their future work. Potential areas for improvement include: clarifying expectations at the outset, translating ideas generated in the curriculum into real-world projects, and optimizing learner experience in a partially virtual platform. A collaborative workshop series between imaging and clinical informatics fellows is the first of its kind in informatics, both in content and teaching style. Continued efforts to increase the quality and reach of such integrated learning are needed to prepare future clinical and imaging informaticists to meet the demands of the highly interdependent modern healthcare system.

Case-based learning (CBL) is widely used in undergraduate medical education, with demonstrable benefits for students across multiple domains. There is a paucity of data in the post-graduate setting. A robust training program is essential for specialty trainees in medical oncology, yet dedicated educational opportunities are limited by a busy clinical environment. This study aimed to evaluate the feasibility of a post-graduate trainee-led CBL program within a Medical Oncology Department at a major cancer centre in Australia. All eligible participants (3 registrars, 1 clinical fellow, 2 nurse practitioners (NPs), 1 post-graduate student NP) were invited to attend weekly CBL sessions over a 20-week period. Session parameters, such as attendance and duration, were recorded. Feasibility was assessed using 4 of Bowen's key areas: acceptability, demand, implementation and practicality. A questionnaire was completed by each participant following each session and on conclusion of the program. Researchers performed statistical analysis using Mann-Whitney U tests and conducted thematic analyses on qualitative data. Of 20 scheduled sessions, 14 (70%) ran without cancellation. Attendance rate was 66%, with most absences being attributed to leave. Mean session duration was 61.4min, with mean preparation time of 24.2min per participant. Participant perceived value was high (9.1/10) and significantly higher with faculty medical oncologist attendance (9.7 vs. 8.8, p = 0.001). Acceptability scores were excellent (9.4/10). CBL sessions were rated significantly higher than concurrent educational opportunities (9.1 vs. 8.1, p < 0.001). Coding of the qualitative data within participant surveys identified the following themes: CBL sessions (1) stimulated relevant and beneficial discussion, (2) were perceived positively, (3) were negatively affected by other clinical commitments, (4) were enhanced by the presence of a faculty medical oncologist, (5) generated a safe space for collaborative learning, (6) were time-efficient and (7) provided self-study opportunities. A postgraduate, trainee-led CBL program in medical oncology is feasible and was well received at our centre. It offers a promising complement to traditional teaching in a rapidly evolving field where faculty oncologist-led educational opportunities may be limited. Future studies could focus on the potential for virtual implementation to allow for collaboration between centres, to the benefit of rural medical oncology trainees.

As a pilot study, we explored how Korean immigrant youths could collaborate with their autistic siblings to support the learning of daily living skills using video-based instruction. The following research questions were addressed: (a) Is there a functional relation, or positive effect, between the training and siblings' fidelity of implementation? (b) Is there a functional relation between the intervention and the autistic youths' completion of self-selected daily living skills? (c) How do autistic youths and their siblings evaluate the social validity of the program? We used a nonconcurrent multiple probe single-case experimental design across three autistic youth-sibling dyads. The autistic youths selected a daily living skill they desire to learn. Siblings created task analyses and video clips for the intervention. After training, siblings implemented video-based instruction embedded with least to most prompts and reinforcement. Siblings' fidelity of video-based instruction increased after training. The autistic youths' percentage of task completion increased upon the implementation of intervention. All three sibling dyads reported high levels of social validity with regard to their preference of the instruction, feasibility, and sustainability. The findings of this pilot study suggest that culturally adapted, family-engaged, video-based training and intervention that are informed, planned, and implemented collaboratively with autistic youths and family members can be beneficial in promoting the youths' daily living skills acquisition while ensuring socially valid approaches.

ABSTRACT In 2021, the Dutch Ministry of Education launched a financial incentive to promote virtual international student collaboration projects, known as Virtual Exchange or Collaborative Online International Learning (COIL). The scheme encouraged educational developers’ involvement in COIL design, although their role in international education is not well understood. This case study explores COIL collaboration among lecturers, an educational developer, and a student coach at a Dutch university of applied sciences. Findings reveal that while the educational developer improved constructive alignment within COIL practices, there is a lack of alignment with broader curriculum internationalisation. Moreover, the collaborative learning aspect in COIL was largely implicit; lecturers lacked familiarity with collaborative learning design and it was unclear whose responsibility it was to address this.

ABSTRACT Teacher educators undertake complex work yet often lack sustained, structured opportunities to engage with deliberate, practice‑embedded professional development. This paper introduces a collaborative professional learning programme to build a shared understanding of identity, pedagogy and context. Preliminary results reveal tensions between multiple roles and expectations, including the caring/wellbeing dimension of teacher education; classroom-related as well as wider systemic constraints; and multifaceted ways of viewing student teachers.

Learning multi-label image classification with incomplete annotations is a challenging task that has been widely studied for its superior trade-off between high efficiency and less labor consumption on large-scale datasets. Predominant methods rely on strong prior assumptions to recover the missing semantics from partial annotations. However, these statistic priors suffer from unstable semantic mistakes and thus lead to catastrophic overfitting. Toward this end, we propose a Language-driven Dense Semantic Adaptor (LDSA) that excavates prior-adaptive relationships from multimodal pretrained CLIP models. In our approach, the densely contrastive adaptor is first proposed to construct dense visual contrastive constraints, transferring the task-specific knowledge to visual domains. We then propose a language-driven interactive decoder with the help of class-specific prompt tuning, which adapts language proxies with visual domains. With the collaborative learning of proposed modules, experimental results demonstrate our proposed LDSA achieves a new state of the art on public multi-label classification benchmarks, and interpretable analyses reveal that our LDSA discovers implicit semantic relationships with the prior-adaptive learning scheme.