Anatomical education is transitioning from the time-honored cadaveric dissection to a blend of learner-centered and technology-enhanced learning approaches. In view of the increased use of various technologies for teaching and learning human anatomy, the aim of this study is to explore students' acceptance of four learning technologies using the technology acceptance model (TAM). This work was conducted at a graduate medical school in Singapore with first-year MD Program students. The acceptances of the four learning technologies were compared in two studies. In Study 1 (n = 46), we compared a 3D-printed (3DP) model with Primal Pictures to answer a clinical question in a Spine Anatomy Tutorial; in Study 2 (n = 55), we compared the Anatomage Table and Primal VR for a Brain Anatomy tutorial. There was a statistically significant preference (p < 0.05) for 3DP models over Primal Pictures for learning Spine Anatomy, and for Primal VR over Anatomage for learning Brain Anatomy. The perceived ease of use of any technology does not appear to influence the behavioral intention to use it. Qualitative feedback suggests that visualization and spatial relationships were among the most important facilitators of learning. Technology should be an enabler in learning but some technologies have a steeper learning curve than others. Therefore, to increase its perceived usefulness, educators must leverage the affordances of the technology when designing learning activities.
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http://dx.doi.org/10.1002/ca.24254 | DOI Listing |
Front Psychol
December 2024
School of Philosophy and Sociology, Jilin University, Changchun, China.
Introduction: Knowledge sharing is an effective means of knowledge management in colleges and universities, which is of great significance for improving the quality and efficiency of universities and enhancing the balanced development of educational resources. The present study investigated the influence students' proactive personalities drive knowledge-sharing activities, and examined the significance of class climate and learning engagement as mediating factors, utilizing the perspectives of social exchange theory (SET) and the job demands and resources model (JD-R) .
Methods: A convenience sampling method was employed to survey 1,053 Chinese college students, and evaluated them using the Proactive Personality Scale (PPS), Learning Engagement Scale (LES), Class Climate Scale (CCS), and Knowledge Sharing Behavior Scale (KSBS).
Exp Ther Med
February 2025
Department of Emergency, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning, Hubei 437199, P.R. China.
Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed.
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December 2024
Zhejiang University, Hangzhou, China.
As the parameter size of large language models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating federated learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers have also tried applying LLMs within FL to tackle challenges in relevant domains.
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December 2024
Data Sciences and Artificial Intelligence Section, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.
The placenta is vital to maternal and child health but often overlooked in pregnancy studies. Addressing the need for a more accessible and cost-effective method of placental assessment, our study introduces a computational tool designed for the analysis of placental photographs. Leveraging images and pathology reports collected from sites in the United States and Uganda over a 12-year period, we developed a cross-modal contrastive learning algorithm consisting of pre-alignment, distillation, and retrieval modules.
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December 2024
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, and . While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.
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