Effects of mobile learning applications for physical examination among nurse students: Use of the cardiovascular system
Year: 2025
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
Abstract: AIM: This study aims to evaluate the effect of cardiovascular physical examination employed on mobile applications in nursing students. BACKGROUND: Mobile learning applications have become a new teaching method; however, few studies on clinical physical examination teaching exist. Improving students' technical ability via exploiting innovative teaching methods to promote autonomous learning and clinical decision-making ability is crucial. DESIGN: A cluster-randomized controlled trial with a pre-test-post-test design was conducted among 82 nursing students enrolled in the five-year junior college program in the College of Nursing, with 42 students assigned to the intervention group and 40 to the control group. METHODS: The intervention group used the mobile application cardiovascular physical examination teaching method (n = 42), while the control group used only the traditional face-to-face teaching method (n = 40), to evaluate students' learning effectiveness. RESULTS: Compared with the control group, the intervention group showed improved skill performance. In addition, the two groups showed no significant differences in Clinical reasoning readiness, self-learning ability, knowledge and learning satisfaction. CONCLUSIONS: Mobile learning applications improve students' skill performance to a greater degree than traditional face-to-face teaching. It is recommended that teachers implement mobile learning applications in nursing courses, test these methods in various settings to evaluate their impact and use these findings to update the curriculum based on current educational needs.
Source: Nurse Education in Practice
Authors: Hui-Mei Chao, Shin-Wen Chen, Shu-Mei Chao
Institutions: National Dong Hwa University, Tzu Chi University
Language: English
Topic: Mobile Learning in Education
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,970
Funder: Tzu Chi University
Award: G6914764629
OpenAlex ID: W4415755583
