Dataset Information

Effects of exergames on student physical education learning in the context of the artificial intelligence era: a meta-analysis.

ABSTRACT: Whether the application of exergames in physical education (PE) courses can significantly improve student performance in PE learning is still controversial. This review explores the promoting effect of exergames on student PE learning and the conditions in which the effect of exergames can be maximized. Based on the PICOS method, two researchers independently searched the ProQuest database, EBSCO database, Web of Science (WoS) database, PubMed database, Chinese National Knowledge Infrastructure (CNKI) database, Wanfang database, and VIP database, evaluated the literature quality using the Cochrane system evaluation manual, and performed a meta-analysis of the included literature. A total of 16 randomized controlled trials involving 2962 subjects were included in this study. The meta-analysis showed that exergames effectively improved student performance in PE learning (SMD = 0.45, 95% CI: 0.27-0.63, P < 0.00001). Subgroup analysis indicated that better results could be achieved when exergames were introduced in small kindergarten classes and continued for 1-2 months.

SUBMITTER: Zhao M

PROVIDER: S-EPMC10965939 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Effects of exergames on student physical education learning in the context of the artificial intelligence era: a meta-analysis.

Zhao Mengnan M Lu Xurui X Zhang Qi Q Zhao Rutong R Wu Bohang B Huang Sheng S Li Sunnan S

Scientific reports 20240326 1

Whether the application of exergames in physical education (PE) courses can significantly improve student performance in PE learning is still controversial. This review explores the promoting effect of exergames on student PE learning and the conditions in which the effect of exergames can be maximized. Based on the PICOS method, two researchers independently searched the ProQuest database, EBSCO database, Web of Science (WoS) database, PubMed database, Chinese National Knowledge Infrastructure ...[more]

PMID: 38531948

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Project description:Introduction: As artificial intelligence (AI) becomes increasingly integrated into various professional fields, understanding its impact on pharmacy education is crucial. This study explores pharmacists' perceptions of AI's role in enhancing educational and professional practices, particularly focusing on the generation of educational content and analytical tasks. Objectives: The primary objective was to assess pharmacists' concerns and perceived benefits regarding the use of AI in pharmacy education, examining variations across different age groups and years of practice. Methods: A cross-sectional survey was completed by 446 pharmacists who actively precept pharmacy residents and students. Respondents practiced across 35 states with over half (53.4%) being in Ohio. The survey included items on concerns about AI's quality and accuracy, human interaction, plagiarism, and its potential benefits in data analysis and research literature summarization. Responses were analyzed to identify trends across demographic categories, including age and years in practice. Results: Of the respondents, 67.9% expressed concerns about the quality and accuracy of AI-generated content, while 50.9% were concerned about plagiarism. Younger pharmacists (73.8% of those aged 20-29) showed heightened concern about accuracy compared to older groups (56.8% of those aged 60+). In contrast, 57.8% of respondents recognized AI's potential benefits for data analysis, with experienced pharmacists (>20 years in practice) being more likely to see these advantages (62.2%). Conclusion: The findings indicate a need for targeted educational strategies to address AI literacy and ethical use in pharmacy education. Integrating AI tools that support educational objectives while addressing these concerns could enhance the efficacy and acceptance of AI in pharmacy practice. Further research should explore the development of training programs that align with the evolving expectations and technological competencies of different pharmacist demographics.

Project description:This study aims to explore new educational strategies suitable for the mental health education of college students. Big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status on the mental health education of college students is introduced. The internet of things (IoT) on mental health education, a structure based on big data and convolutional neural network (CNN), is constructed. Next, the survey design and questionnaire survey are carried out. Finally, the questionnaire data are analyzed and compared with the mental health status under traditional education. The results show that the CNN model has good accuracy and ability to distinguish symptoms, so it can be applied to the existing psychological work in colleges. In the symptom comparison survey, under the traditional education and big data network, the number of college students with mild mental health problems is found to be 158 (84.9%) and 170 (91.4%), respectively. It indicates that the number of college students with moderate mental health problems decreases significantly. In the comparative investigation of the severity of mental problems, the number of students with normal mental health, subhealth, and serious mental health problems under the background of traditional mental health education is 125 (67.2%), 56 (30.1%), and 5 (2.7%), respectively. The mental health status of college students under the influence of big data networks on mental health education is better than that of traditional mental health education. There are 140 students with normal mental health, a year-on-year increase of 16.7%. In the comparative survey of specific mental disorders, students with obsessive-compulsive symptoms under traditional mental health education account for 22.0% of the total sample, having the largest proportion. In the subhealth psychological group under the big data network on mental health education, the number of hostile students decreases by 7, which is the psychological factor with the most obvious improvement. Hence, the proposed path of mental health education is feasible.

Dataset Information

Effects of exergames on student physical education learning in the context of the artificial intelligence era: a meta-analysis.

Publications

Effects of exergames on student physical education learning in the context of the artificial intelligence era: a meta-analysis.

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