Making memories malleable: The role and regulation of synaptic depotentiation in engram alteration.

Beyond traditional education: can public-private partnership-based recognition of prior learning transform education in developing countries

Beyond traditional education: can public-private partnership-based recognition of prior learning transform education in developing countries

While numerous studies have highlighted the benefits of AI in language education, the research examining its effectiveness through the lens of personality factors remains limited. Individual personality traits shaping how learners perceive and utilize technology play a critical role in AI-based learning outcomes. This study investigated the relationship between personality traits and AI efficacy in Arabic language learning, framed within the Big Five Personality Traits model (extraversion, openness, conscientiousness, agreeableness, and neuroticism). Through a qualitative survey approach, the data were collected from 27 university students with prior Arabic learning experience (2–4 years) and AI familiarity. Participants included 26 students (96.3%) from pesantren (Islamic boarding schools) and 1 student (3.7%) from a madrasah, all of whom completed questionnaires assessing their perceptions of AI’s role in learning across personality dimensions. Findings reveal that AI enhances learning comfort, adapts to diverse styles, improves grammatical and lexical mastery, and boosts motivation while fostering traits like independence, diligence, and creativity. However, its effectiveness depends on users’ digital literacy, intrinsic motivation, and capacity for reflective learning. The study highlighted the necessity of personalized AI integration in language education, emphasizing adaptive pedagogical support to address individual differences. These insights contributed to the discourse on AI-driven education by bridging personality psychology with technology-enhanced learning, offering practical implications for designing inclusive and effective AI tools in higher education.

This study aims to analyze the learning strategies used by non-English major students to improve their English-speaking skills, as well as to identify the factors influencing their choice of strategies and the challenges they encounter during the learning process. The research employed a qualitative approach with a case study design. Data were collected through in-depth interviews, observations, and documentation involving several non-English major students who actively practiced speaking English. The findings revealed that students applied various learning strategies, including cognitive, metacognitive, social, and affective strategies. Factors influencing strategy selection included motivation, prior learning experiences, social support, and access to technology. Meanwhile, the main challenges faced by students were limited vocabulary, fear of making mistakes, and lack of opportunities to practice speaking. Overall, the study highlights that non-English major students are capable of improving their speaking skills through independent, creative, and context-based learning strategies, despite the constraints of formal learning environments.

This study investigates the correlation between clinical instructor characteristics and the development of clinical competencies in students within healthcare disciplines such as nursing, medicine, and allied health. Effective clinical instruction is essential in bridging theoretical knowledge with real-world application, and the clinical instructor plays a pivotal role in this learning process. The study aims to identify which instructor traits are most strongly linked to positive student outcomes in clinical education. The investigation will examine various instructor characteristics, including teaching style (didactic, facilitative, coaching), clinical experience, specific expertise, communication and interpersonal skills (e.g., empathy, clarity, approachability), feedback delivery, organizational ability, capacity to foster a supportive learning environment, and personality traits (e.g., enthusiasm, patience, adaptability). Additionally, demographic factors such as age, gender, and educational background will be considered to assess their potential influence. Student clinical competency will be evaluated through direct observation, standardized checklists, self-assessments, clinical assignments, and feedback from other clinical staff. The competencies measured will align with established professional standards and program-specific learning objectives. A correlational research design will be employed, using statistical analyses to determine the strength and direction of relationships between instructor characteristics and student competency development. The study will control for confounding variables such as prior academic performance, learning styles, and student motivation. It will also examine whether specific instructor attributes are more strongly associated with particular types of competencies, such as technical proficiency, critical thinking, or communication. Findings from this research will have practical implications for the recruitment, selection, and development of clinical instructors. By identifying the traits that contribute to effective teaching, healthcare institutions can enhance their clinical education programs and ensure students are well-prepared to meet the demands of professional practice.

Accurate survival prediction for urothelial carcinoma (UC) is critical but limited by a lack of AI systems that integrate multimodal data with prior knowledge. To address this gap, we developed a multimodal deep learning system that integrates histopathology, radiology, and structured pathology text. We incorporated prior knowledge to improve tumor segmentation and create knowledge-guided slide representations. CTContextNet and MacroContextNet were then employed for capturing radiological and macroscopic prognostic information. IM-NCTNet integrated multi-modality information for enhanced prediction. Across multi-center, large-scale, multi-cohort validation, the knowledge-guided prognostic system demonstrated higher performance over single-modality models, achieving C-index scores ranging from 0.809 to 0.867. The framework identified novel prognostic biomarkers related to infiltration patterns in muscle and renal parenchyma. High-risk Coloc_M, Coloc_R, and IMTS groups exhibited increased mortality risk, with hazard ratio values ranging from 2.47 to 16.38. The proposed AI framework offers a comprehensive and robust tool for UC prognosis, supporting refined patient management.

BackgroundAlzheimer's disease (AD) is characterized by early neuropathological changes in the entorhinal cortex (EC), which plays a crucial role in spatial navigation and probably abstract space navigation. While EC‐dependent navigation tasks show promise for early AD detection, existing paradigms require extensive learning periods, limiting clinical utility. We aimed to develop and validate a novel digital non‐spatial navigation task that tests EC function without requiring prior learning, hypothesizing that performance would correlate with hippocampal‐entorhinal cortex dependent memory but not other cognitive domainsMethodThe study enrolled 38 participants, aged 40‐70 years‐old, who performed a computerized task navigating 2D abstract space using stripe and ellipse width dimensions. The task comprised 96 trials, each presenting three successive visual stimuli forming a trajectory, with participants selecting the correct fourth stimulus from three choices. Performance was assessed against standard cognitive measures, including MoCA, CDR, VPA delayed recall, WCST, Go/No‐Go test, and Spatial Span.ResultPerformance decreased with increasing difficulty in distance deviation trials, suggesting navigation strategy usage. Trials with angular deviation showed possible prepositional strategy involvement. Task accuracy analyzed only with trials with distance deviation significantly correlated with episodic memory measures: MoCA delayed recall (r = 0.425, p = 0.008), MoCA memory index score (r = 0.431, p = 0.007), VPA delayed recall (r = 0.377, p = 0.020), and CDR memory (r = ‐0.535, p = 0.001). No significant correlations emerged with non‐memory cognitive domains. Correlations persisted after adjusting for age and education, except for VPA delayed recall.ConclusionThe study demonstrates that this novel non‐spatial navigation task probably engages EC‐hippocampal circuits, as evidenced by selective correlation with episodic memory measures. The task's design, requiring no prior learning period, represents a significant advantage over existing paradigms and potential clinical utility. Future research should investigate concurrent neural activities and evaluate diagnostic value participants with confirmed AD biomarkers.

Producers learn from each other by sharing their experiences with implementing management practices – business, personnel, production - on their farms, while positively contributing to mental health and well-being (Betker and Lemoine, 2022; Farm Management Canada, 2012). The purpose of this project was to pilot peer learning groups to test their effectiveness as an alternative way to reach producers and encourage the involvement of early adopters in knowledge diffusion. Peer groups, comprised of Saskatchewan livestock producers, were formed to establish a peer-to-peer connection. Each peer group consisted of 5-8 operations, varying in their production model and location in the province. Meetings were organized throughout the year, being mainly virtual, with an option of one or two in-person meetings for each group. WhatsApp chats were created to encourage discussion outside of the meetings. Groups identified learning priorities for meetings based on mutual interests. Meetings were 90 minutes long and consisted of upcoming event notifications, guest speakers, and facilitated discussion. E-mails emphasizing key messages and relevant information were shared after every meeting. The peer group model is being expanded, with plans to create groups targeted to certain demographics, starting with young producers. An average of 60% of peer group members consistently attended their group’s meetings. Two-thirds of participants in the first iteration of peer groups had never previously participated in a peer group, and all were somewhat or very likely to recommend peer group participation to a friend. These groups amplified extension messaging and provided a sense of community among the members. Utilizing technology to form peer groups can increase knowledge diffusion by bringing early adopters from across the province together to share their experiences. This amplifies extension of practices and increases the speed of adoption on a larger scale.

Research on the learning- and engagement-related impacts of choice in assessment is growing, though it often focuses on a single kind of choice with insufficient attention to the learning priorities and motivations behind students’ decisions. The assessment design explored here offered flexibility to students by providing multiple kinds of choice while maintaining the critical structure of a single type of assignment, which was necessary to preserve learning outcomes and not overwhelm students. This study investigated the impact of choice not only on students’ perceptions of their learning and engagement but also on their decision making process in order to better understand their motivations and the influences of learning priorities and individual circumstances. Guided by a mixed methods approach involving a survey followed by interviews, we found that offering choice in assessment increased students’ perceived autonomy and control, ability to balance other demands, quality of work, and enjoyment and interest in the work, while decreasing their stress levels. We also found that offering multiple elements of choice in a single assessment supported students’ abilities to make decisions based on their complex and diverse learning priorities, motivations, and individual circumstances. This study, as well as research on flexible assessment more broadly, respond to the International Society for the Scholarship of Teaching and Learning’s grand challenges, which include a call for more investigation into student engagement in learning and the complex processes of learning.

Background Public health education in India faces several challenges like non-uniform curricula, limited emphasis on experiential and competency-based learning, and inadequate infrastructure. Addressing these challenges demands significant changes in public health education to align it with Sustainable Development Goals 4 and National Education Policy (NEP) 2020 in particular. To overhaul the Masters of Public Health (MPH) program with a focus on holistic and multidisciplinary approaches, objective of this paper was to propose a framework for MPH program in alignment with NEP 2020. Methods A thorough desk review of NEP 2020 for reforms in higher education was conducted. Nine documents by University Grants Commission (UGC) detailing implementation of the policy at higher education level were reviewed for their relevance to MPH programs. Various guidelines to align MPH programs with policy’s proposed reforms were identified. MPH framework was developed on the basis of these guidelines. Results MPH programs in India are not in alignment with NEP 2020 reforms. The framework suggested in this study includes guidelines on: (1) Flexible career pathways, (2) multiple entry and multiple exits, (3) levels, level descriptors, qualification specifications and graduate attributes, (4) credit system and Academic Bank of Credit, (5) evaluation and assessment, (6) Incorporation of Massive Open Online Courses (MOOC), Indian Knowledge System (IKS) Courses and Courses on Human Values and Ethics, and (7) provisions for recognition of prior learning (RPL) thus aligning the MPH program with selected policy reforms. Conclusion MPH programs can be aligned with certain reforms suggested by NEP 2020. The proposed framework can provide a solid background for transforming MPH programs in India, making them more flexible, interdisciplinary, research oriented and improving their quality.

This article examines the comprehensive educational framework for Public Management and Administration at Zhytomyr Ivan Franko State University, spanning bachelor's, master's, and doctoral levels. The study focuses on the program's innovative approach to cultivating a new generation of public administrators equipped to address contemporary governance challenges. Particular emphasis is placed on the program's unique integration of academic expertise and practical governance experience through its faculty composition, which includes former senior government officials, parliamentary deputies, and distinguished scholars from the National Academy of Public Administration. The research details the implementation of progressive pedagogical methodologies, including innovative assessment techniques and experiential learning opportunities. A significant aspect of the program involves strategic partnerships with regional governance structures, facilitating student immersion in practical governance through internships and collaborative projects across Zhytomyr Oblast. The article argues that this integrated approach represents a significant advancement in public administration education, effectively bridging theoretical knowledge and practical application while addressing regional development needs. The structural design of these educational-professional and educational-scientific programs demonstrates a clear alignment with contemporary labor market demands and the evolving needs of the public sector. The curriculum is strategically developed to cultivate a new generation of specialists equipped for multifaceted roles within executive bodies, local self-government institutions, and the non-governmental sector. Beyond preparing graduates for traditional administrative functions, the programs place significant emphasis on fostering research and innovation capabilities. This dual focus aims to develop professionals who can not only implement existing administrative procedures but also generate new, evidence-based knowledge and initiate transformative professional practices within public administration. A central objective of the program is to equip graduates with the competencies necessary to ensure the delivery of administrative services that meet socially acceptable standards. This entails instilling a deep commitment to the core principles of modern governance: openness, transparency, accountability, and professionalism. The curriculum is designed to be resilient and forward-looking, preparing students to operate effectively both in the current, challenging conditions and in the complex environment of post-war recovery and reconstruction. The program's content is characterized by its logical coherence and rigorous alignment with the subject area of specialty D4 "Public Management and Administration." It provides a balanced and robust foundation in both theoretical concepts and applied practical skills. This ensures that graduates are not merely executors of tasks, but are critical thinkers and capable managers, prepared to lead and administer public sector organizations effectively. A defining feature of the educational framework is its commitment to flexibility through the implementation of individualized educational trajectories. This approach acknowledges the diverse career goals, prior experiences, and research interests of students. The mechanism for personalization is realized through several key instruments: Students are offered a wide selection of specialized courses and modules, allowing them to tailor their learning to specific areas of interest within public administration, such as healthcare management, regional development, or digital governance. The program facilitates practical experiences in various types of institutions (state authorities, local councils, NGOs), enabling students to align their internships with their professional aspirations. Provisions for individual schedules and the recognition of prior learning outcomes provide additional flexibility, catering to the needs of different student cohorts. Opportunities for participation in national and international exchange programs further enrich the individual learning path, exposing students to diverse administrative practices and academic environments. This learner-centered model empowers students to construct an educational experience that is directly relevant to their personal and professional ambitions, thereby enhancing the efficacy and relevance of their training. This operational framework for individualized learning is underpinned by a systematic approach to choice architecture. Students exercise agency in selecting not only elective courses but also the specific pedagogical formats—such as project-based seminars, policy labs, or traditional lectures—that best align with their learning preferences. The diversity of educational components ranges from foundational theoretical modules to advanced specialized tracks, each calibrated for varying levels of complexity to accommodate both early-career professionals and seasoned practitioners. The program's pedagogical design intentionally cultivates higher-order cognitive capabilities essential for public sector innovation. This includes structured training in heuristic problem-solving for ill-defined administrative challenges, methodologies for policy prototyping, and systematic approaches to organizational change management. A distinctive feature is the integration of research-led teaching, where students actively engage in applied policy analysis and develop evidence-based interventions for real-world public management issues. Furthermore, the curriculum embeds rigorous training in academic research methodologies, positioning graduates to contribute meaningfully to the scholarly discourse on public administration. This research competence extends beyond technical skill acquisition to encompass the development of original conceptual frameworks and empirical studies that address substantive gaps in both theoretical understanding and professional practice. The emphasis on generating knowledge with demonstrable scientific novelty and practical impact ensures graduates can function effectively at the intersection of academic inquiry and administrative innovation.

While blended learning facilitates digital literacy development, the specific design models and student factors contributing to this process remain underexplored. This study examined the relationship between various blended learning design models and digital literacy skill acquisition among 106 upper-secondary Vocational Education and Training (VET) students. Relationships among student activities, digital competencies, and prior blended learning experience were analyzed. Engagement in collaborative, task-based instructional designs—specifically collaborative projects and regular quizzing supported by digital tools—was positively associated with digital competence. Conversely, passive participation in live sessions or viewing pre-recorded videos exhibited a comparatively weaker association with competence development. While the use of virtual/augmented reality and interactive video correlated positively with digital tool usage, it did not significantly predict perceptions of online safety or content creation skills. Students with prior blended learning experience reported higher proficiency in developmental competencies, such as content creation and research, compared to their inexperienced peers. Cluster analysis identified three distinct student profiles based on technical specialization and blended learning experience. Overall, these findings suggest that blended learning implementation should prioritize structured collaboration and formative assessment.

Introduction: Cesarean delivery, peripartum hysterectomy, female genital fistula treatment, and long-acting and permanent contraceptive method provision comprise an important set of surgical procedures in reproductive and maternal health. The volume of these procedures is growing in low- and middle-income countries (LMICs). Establishing research priorities in a learning agenda for surgical obstetrics and family planning represents a key step in generating and using evidence to improve health outcomes associated with these surgeries.Methods: Between January and February 2022, a safe surgery project addressing family planning and obstetrics used a 2-stage rating and ranking consultation process to prioritize topics in its learning agenda, focusing on LMIC needs. A list of research and learning topics spanning the project's technical areas, consisting of surgical obstetric care (cesarean delivery and peripartum hysterectomy), fistula prevention and treatment, family planning, and cross-cutting safe surgery, was curated by searching the literature, conducting project-related surveys of experts and partners, and soliciting an expert panel via virtual consultation. Through an online survey, the experts rated the 63 topics on a 5-point scale based on 4 criteria-feasibility, technical importance, level of saturation, and potential for impact-and average ratings were calculated for each criterion and topic. The expert panel then reconvened virtually to rank and refine highly rated topics.Results: A total of 39 people participated in the expert panel, representing multilateral, academic, and funding organizations, implementing partners, and professional associations active in LMICs. Fifteen topics were prioritized across the 4 technical areas. Prioritized topics covered themes of prevention (e.g., intrapartum/midwifery practices to prevent unnecessary cesarean delivery), care-seeking (e.g., social and behavior change strategies for fistula prevention), perioperative care (e.g., use of quality improvement tools including checklists and audits), and postoperative care (e.g., effective measurement approaches for monitoring outcomes).Conclusion: This agenda guides clinical and programmatic learning across the safe surgery ecosystem. Collaborative action across program initiatives and clinical and community settings may contribute to significant evidence building in these priority topics.

Human physiology is a core subject in nursing education, providing essential knowledge for understanding the human body and applying this knowledge in clinical practice. Despite its importance, many nursing students face difficulties in mastering complex physiological concepts, leading to gaps between theoretical knowledge and practical application. Addressing these challenges is crucial to ensure that future nurses are adequately prepared to deliver safe and effective patient care. This study aimed to evaluate nursing students’ understanding of human physiology concepts in basic biomedical science education and to explore differences based on gender and semester level. A descriptive cross-sectional design was conducted with 50 nursing students from the second and fourth semesters at Famika University. Data were collected using a validated 25-item multiple-choice physiology test. Content validity was reviewed by experts, and a pilot test yielded a Cronbach’s alpha of 0.82, confirming good reliability. Data were analyzed using SPSS version 26 with descriptive statistics, independent t-tests, and chi-square tests, applying a significance level of p < 0.05. The results showed that second-semester students achieved higher comprehension scores (77.75%, Good) than fourth-semester students (68%, Fair). Male students also performed better (81.25%, Good) compared to female students (74.52%, Fair). These findings suggest that variations in students’ understanding may be influenced by academic workload, prior learning experiences, and learning motivation. Strengthening physiology education through active learning strategies and better integration of biomedical science content into the nursing curriculum is therefore recommended.

BackgroundMedical professionals, from trainees to practicing clinicians, often lack adequate preparation to provide care and understand the health needs of transgender and gender diverse (TGD) people, as documented worldwide. As a result, many medical trainees feel unprepared to deliver competent and affirming care to TGD patients. To address this educational gap, this study implemented a simulation-based learning activity as an initial exposure to TGD healthcare. We aimed to assess medical students’ attitudes and performance in a transgender healthcare simulation scenario.MethodsWe designed and evaluated a simulated scenario for the care of a transgender man with delayed menstruation, with the aim of training Obstetrics and Gynecology (OB/GYN) clerkship students in caring for TGD patients. The simulation and cross-sectional data collection took place in a university hospital located in the northeast of Brazil. From January to June 2023, the OB/GYN clerkship medical students were invited to run a simulated scenario and completed a brief quantitative, survey-based evaluation using a simplified “Scale of Satisfaction with Simulated Clinical Experiences”.ResultsOverall, many students reported limited prior experience in caring for transgender and gender diverse patients. For instance, only a small proportion had received formal instruction or had curricular content addressing the distinction between gender identity and sex assigned at birth during their undergraduate program. Similarly, many students acknowledged that sexual orientation may influence the care patients receive. The scenario simulated a consultation for a trans man with secondary amenorrhea, with two trans men participating as standardized patients. Of the 25 undergraduate students taking part, most students demonstrated behavior free from biases or assumptions about patients’ identities, although 44% reported difficulty using the patient’s chosen name and pronouns due to lack of training, and 60% found it challenging to provide counseling on health promotion and prevention. During debriefing, students expressed discomfort discussing sexual practices and limited knowledge about referral pathways for TGD care services. In general, students found certain aspects of the simulation challenging primarily due to limited prior training, with specific examples described above. Students generally responded positively to the simulation, indicating engagement and willingness to learn.ConclusionsSimulation-based learning on transgender healthcare is an effective educational tool for medical students with limited prior exposure to TGD topics. Although students faced challenges, particularly in using correct pronouns, discussing sexual practices, and providing preventive counseling, they demonstrated engagement and receptivity to the scenario. These findings suggest that realistic simulation scenarios can enhance cultural competence and prepare students to provide affirming care to TGD individuals, regardless of prior experience learning about and working with this community.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12909-025-08235-y.

This article discusses Recognition of Learning Outcomes (RPL), also known as Recognition of Prior Learning, as a strategic initiative to bridge the gap between individuals' actual competencies acquired through non-formal, informal, and work experiences and formal higher education qualifications. Supported by Minister of Education, Culture, Research, and Technology Regulation No. 41 of 2021, RPL aims to provide academic recognition and expand access to education for experienced individuals, in line with the concept of lifelong learning. The implementation of RPL is closely related and integrated with the Outcome-Based Education (OBE) approach, where recognition is not based on the learning process, but rather on evidence of outcomes demonstrating competency equivalence with the Graduate Learning Outcomes (CPL) of the study program. However, this implementation faces challenges in Quality Assurance, which must be addressed through a strong institutional role, transparent, fair, and reliable assessment procedures, and the professionalism of RPL Assessors to maintain the credibility of recognition results and academic integrity.

What makes ALEKS learning successful?: influences of prior knowledge, learning time, self-efficacy, and resource management on learning achievement

Acceleration, compacting the curriculum, differentiation, and asynchronous development are just some of the many concepts that underpin the discipline of gifted education and its associated pedagogy. There is little discussion or indeed implementation of these strategies within the tertiary sector. What would tertiary education in Australia look like for students if their learning needs were actively addressed? Can we provide a tertiary learning environment for gifted and dual- or multi-exceptional students by acknowledging their zone of proximal development where learning occurs, and their prior learning is truly recognised and credited? Capable NZ, a school within Otago Polytechnic in New Zealand, provides a model that reveals a potential opportunity for universities in Australia – if we are prepared to truly assess what will work for this important cohort of learners, and to change accordingly.

Abstract Accurate seismic phase picking is critical for earthquake monitoring, tomography, and early warning systems. While prior deep learning models have achieved high performance on benchmark data sets, their applicability in real‐world, continuous waveform scenarios remains limited—particularly for phases beyond Pg and Sg. In this study, we develop a deep neural network trained on the Comprehensive Data set of the Chinese Seismic Network (CSNCD), the largest labeled seismic data set in the world, to simultaneously detect Pg, Sg, Pn, and Sn phases from continuous seismic waveform data. Our model employs an extended input window (102.40 s) and combines convolutional and recurrent architectures to capture long‐range temporal dependencies. It achieves F1‐scores of 0.832(Pg), 0.864(Sg), 0.814(Pn), and 0.491(Sn) on the CSNCD test set. When the analysis window fully contains the seismic signal, the and F1‐scores increase to 0.888 and 0.692, respectively. Operational deployment across Chinese mainland further demonstrates the model's robustness. In most provinces in China, our model achieves higher recall than PhaseNet, while significantly reducing false positives. This work presents the first nationwide real‐time application of a deep learning‐based multi‐phase seismic picker in China, offering a practical and scalable solution to support earthquake monitoring and subsurface imaging.