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In traditional dance teaching, teachers' demonstration generally takes up most of the in‐class time, which reduces the opportunities for students to practice. Therefore, researchers have started to adopt flipped learning to increase the in‐class time for students' practice and teachers' guidance. However, there are some problems associated with the conventional approach to flipped learning; these problems include students' lack of preparation before class which affects their engagement in the in‐class activity; lack of guidance when self‐learning; no instant assistance when encountering problems; and lack of reflection. To address these problems, the current study proposes a reflective thinking promoting approach to instruct the students to understand the pre‐class materials and to strengthen their reflection and the interflow among peers in flipped learning. A total of 129 university students were recruited in the present study. They were divided into the experimental group which adopted the annotation, reflection, questioning and interflow (ARQI) approach and the control group which adopted the previous flipped learning approach. The results revealed that ARQI could significantly enhance the students' dance performance. The interview also revealed that ARQI promoted the students' reflection. Moreover, the higher self‐efficacy students tended to gain more benefits from the flipped learning approach than those with lower self‐efficacy. Lastly, following the ARQI steps to finish the pre‐class learning, students were aware that they were required to put more mental and temporal efforts into the class. [ABSTRACT FROM AUTHOR]

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research., Competing Interests: The authors have declared that no competing interests exist.

Aims: To identify and analyze the 100 most cited articles on nursing student education (NSE) that were published in nursing journals., Background: The citation frequency of an article indicates its scientific impact. Analyzing highly cited articles on NSE enabled researchers to identify mainstream research types and acquire relevant information and knowledge., Design: A retrospective bibliometric analysis with co-citation network analysis., Data Sources: We searched for journal articles on NSE from the Web of Science, and analyzed the 100 most cited articles (date of search: June 3, 2020)., Review Methods: The theme, article type, journal, country, source of institutional articles, and publication year were counted and analyzed. The citation relationships among the most 100 cited articles of NSE were calculated and visualized., Results: Articles included in this study were mostly empirical quantitative studies with a cross-sectional design, and were published in international nursing journals between 2000 and 2020. Their authors were mostly from the United States and the United Kingdom. According to the co-citation network analysis, four main research directions were identified: workplace mental health, simulation learning, spiritual care, and medication administration., Conclusion: Workplace mental health was the major research topic on NSE. This research topic has spanned two decades (2000-2020). These articles were mainly published in the Journal of Advanced Nursing and Nurse Education Today. Some recent research topics, such as simulation learning and spiritual care were also identified. Findings provide a fresh review of the highly cited NSE articles, including the visualization of the NSE network., (Copyright © 2020 Elsevier Ltd. All rights reserved.)