RESUMO
Model-based learning and teaching are vital for addressing real-world challenges and are gaining research traction. This study, employing CiteSpace, analyses 583 articles, uncovering trends in authors, regions, and highly cited documents. Noteworthy focuses include learning achievements, technical support, and teaching approaches. Keyword analysis emphasises thinking cultivation and interdisciplinary integration. The study discusses current and future developments in modelling and modelling education research, particularly in learning evaluation and teacher professional development. Offering an international perspective, this analysis provides stakeholders with valuable insights. In summary, model-based learning's growth and influence are evident in the identified trends and future directions, guiding the field toward effective teaching strategies and solving complex problems. This research contributes to the broader understanding of modelling education's dynamics, facilitating informed decision-making for educators and policymakers.
RESUMO
To comprehensively assess the impact of K-12 engineering education research (K-12 EER) and provide insights to stakeholders and policymakers, this study uses the scientometric analysis software CiteSpace to evaluate recent advances in K-12 EER. A search of 885 articles from the Web of Science Core Collection was conducted. This study analyzed publication trends, authors, countries, and research institutions to determine the trajectory of the K-12 EER. In addition, keyword co-occurrence and clustering maps were generated to identify major hotspots, and keyword burst detection was used to demonstrate development trends. The analysis reveals that global interest in the K-12 EER is growing, and the results are accumulating rapidly. However, cooperation between universities, institutions, experts, and scholars must be strengthened. Current significant topics in K-12 EER focus on the practical form of engineering education, its impact on learners, the centrality of engineering design, and teachers' professional development. Research frontiers primarily revolve around the nature of engineering. Future research efforts should promote the systematic integration of K-12 engineering education through the learning process, develop comprehensive measurement tools to assess its impact on learners, and expand research on teachers' professional development in K-12 engineering education.
RESUMO
The Learning Cycle Model (LCM) is an inquiry-based teaching strategy and curriculum model that has been researched and practiced for almost half a century and has achieved good educational results in science education, especially in primary and secondary schools. This study collected LCM educational research data to better understand the recent progress of LCM educational research. It used the bibliometric analysis software CiteSpace for the first time to sort out the overall development of LCM. Compared to other literature review methods, we obtained a more comprehensive picture of the current research hotspots and future research trends in LCM research. First, we searched 498 articles from the Web of Science core collection between 2000 and May 16, 2023. The trajectory of LCM research was identified by analyzing publication trends, authors, countries/regions, and research institutions. Secondly, we obtained the keyword co-occurrence map and clustering map through CiteSpace's built-in algorithm to analyze the main areas and research frontiers of this study. Meanwhile, the development trend is shown by co-keyword burst detection. After analysis, this study found that the current research hotspot findings focus on conceptual learning, validity research, and integration with different teaching processes. The research frontiers are mainly related to teacher professional development and research on the impact of LCM on learning outcomes. Finally, this study discusses future research directions, including research on the impact of LCM on 21st century skills, comparison with other modes of inquiry teaching and learning, and its use in engineering education, to promote better development of LCM.
RESUMO
Perioperative neurocognitive disorder (PND) is a common surgery outcome affecting up to a third of the elderly patients, and it is associated with high morbidity and increased risk for Alzheimer's disease development. PND is characterized by cognitive impairment that can manifest acutely in the form of postoperative delirium (POD) or after hospital discharge as postoperative cognitive dysfunction (POCD). Although POD and POCD are clinically distinct, their development seems to be mediated by a systemic inflammatory reaction triggered by surgical trauma that leads to dysfunction of the blood-brain barrier and facilitates the occurrence of neuroinflammation. Recent studies have suggested that the gut microbiota composition may play a pivotal role in the PND development by modulating the risk of neuroinflammation establishment. In fact, modulation of gut microbiome composition with pre- and probiotics seems to be effective for the prevention and treatment of PND in animals. Interestingly, general anesthetics seem to have major responsibility on the gut microbiota composition changes following surgery and, consequently, can be an important element in the process of PND initiation. This concept represents an important milestone for the understanding of PND pathogenesis and may unveil new opportunities for the development of preventive or mitigatory strategies against the development of these conditions. The aim of this review is to discuss how anesthetics used in general anesthesia can interact and alter the gut microbiome composition and contribute to PND development by favoring the emergence of neuroinflammation.