BMC nursing · 2026
Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.
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Zhang Yanting, Zhang Pu, You Jinkun, Xiao Meng, Zhang Wei, Li Zhaoyang, Li Hanqing, Tian Chao, Li Jin, Ding Xinbo, Ma Jing, Zheng Anlong
Аннотация
OBJECTIVE: Given the high alignment between deep learning and blended teaching objectives, blended teaching provides a feasible pathway for achieving deep learning. This study aimed to verify the efficacy of a deep learning-based blended teaching model integrating online immersive learning, offline scenario simulation and clinical case review in improving ECMO transport competency among ICU nurses.
METHODS: This was a single-center prospective parallel-group randomized controlled trial. A total of 130 ICU nurses were randomly assigned to experimental group (n = 68) and control group (n = 62). The experimental group received the blended teaching intervention, while the control group adopted traditional offline lecture teaching. Baseline questionnaires, learning style and motivation scales were collected before intervention. Eye-tracking technology and engagement scales were used to evaluate learning immersion during training. Post-training knowledge level and satisfaction were assessed, and paired t-tests were adopted for within-group comparison.
RESULTS: Baseline data were comparable between the two groups. The experimental group showed significantly better eye-tracking immersion indicators and higher online learning participation than the control group (all P < 0.001). Post-training knowledge scores were significantly improved in the experimental group, while no obvious change was found in the control group. The intervention presented a small positive effect size. No significant between-group differences were observed in learning engagement, cognitive participation and satisfaction.
CONCLUSION: Compared with traditional offline teaching, the deep learning-oriented blended teaching model can effectively improve ICU nurses' ECMO transport knowledge mastery, learning immersion and online learning participation. It makes up for the competency deficiency of ICU nurses in ECMO transport and provides evidence for optimizing clinical nursing training strategies.
TRIAL REGISTRATION: Chinese Clinical Trial Registry, Registration Number: ChiCTR2500115168, Public Access Link https://www.chictr.org.cn/showproj.html?proj=292586 . The registration date is December 23, 2025.
METHODS: This was a single-center prospective parallel-group randomized controlled trial. A total of 130 ICU nurses were randomly assigned to experimental group (n = 68) and control group (n = 62). The experimental group received the blended teaching intervention, while the control group adopted traditional offline lecture teaching. Baseline questionnaires, learning style and motivation scales were collected before intervention. Eye-tracking technology and engagement scales were used to evaluate learning immersion during training. Post-training knowledge level and satisfaction were assessed, and paired t-tests were adopted for within-group comparison.
RESULTS: Baseline data were comparable between the two groups. The experimental group showed significantly better eye-tracking immersion indicators and higher online learning participation than the control group (all P < 0.001). Post-training knowledge scores were significantly improved in the experimental group, while no obvious change was found in the control group. The intervention presented a small positive effect size. No significant between-group differences were observed in learning engagement, cognitive participation and satisfaction.
CONCLUSION: Compared with traditional offline teaching, the deep learning-oriented blended teaching model can effectively improve ICU nurses' ECMO transport knowledge mastery, learning immersion and online learning participation. It makes up for the competency deficiency of ICU nurses in ECMO transport and provides evidence for optimizing clinical nursing training strategies.
TRIAL REGISTRATION: Chinese Clinical Trial Registry, Registration Number: ChiCTR2500115168, Public Access Link https://www.chictr.org.cn/showproj.html?proj=292586 . The registration date is December 23, 2025.