Effects of Flipped, Web-Based, and Traditional Learning Models on Clinical Nurses’ Self-Directed Learning and Metacognition

Global health crises, armed conflicts, and technological advancements such as artificial intelligence have made nursing care more complex, requiring clinical nurses to develop stronger self-directed learning and metacognitive thinking skills. However, most educational interventions focus on nursing students, with limited attention given to clinical nurses and the use of innovative learning methods. This study examined the effects of evidence-based practice training delivered through flipped learning, web-based learning, and traditional classroom methods on clinical nurses' self-directed learning and metacognitive thinking. A randomized controlled trial was conducted with 45 clinical nurses in a training and research hospital. Participants were randomly assigned to 1 of 3 groups: flipped learning, web-based learning, or traditional instruction. Data were collected using validated scales before the intervention, immediately after, and 1 month later. Findings indicated that both flipped and web-based learning significantly improved nurses' self-directed learning and metacognitive thinking skills compared to traditional learning. No significant difference was found between the flipped and web-based models. These results suggest that innovative, flexible learning approaches can enhance evidence-based practice capabilities among clinical nurses and should be integrated into continuing education programs to improve quality of care in clinical settings.

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Publication Details

Journal
CIN Computers Informatics Nursing
Published
2026-01-05
DOI
https://doi.org/10.1097/cin.0000000000001464
Primary Topic
Innovative Teaching Methods
Type
article
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article

Effects of Flipped, Web-Based, and Traditional Learning Models on Clinical Nurses’ Self-Directed Learning and Metacognition

Emine Akçin Şenyuva, Deniz Yildirim
CIN Computers Informatics Nursing
Innovative Teaching Methods
article

Effects of Flipped, Web-Based, and Traditional Learning Models on Clinical Nurses’ Self-Directed Learning and Metacognition

Emine Akçin Şenyuva, Deniz Yildirim
article en

Abstract

Global health crises, armed conflicts, and technological advancements such as artificial intelligence have made nursing care more complex, requiring clinical nurses to develop stronger self-directed learning and metacognitive thinking skills. However, most educational interventions focus on nursing students, with limited attention given to clinical nurses and the use of innovative learning methods. This study examined the effects of evidence-based practice training delivered through flipped learning, web-based learning, and traditional classroom methods on clinical nurses' self-directed learning and metacognitive thinking. A randomized controlled trial was conducted with 45 clinical nurses in a training and research hospital. Participants were randomly assigned to 1 of 3 groups: flipped learning, web-based learning, or traditional instruction. Data were collected using validated scales before the intervention, immediately after, and 1 month later. Findings indicated that both flipped and web-based learning significantly improved nurses' self-directed learning and metacognitive thinking skills compared to traditional learning. No significant difference was found between the flipped and web-based models. These results suggest that innovative, flexible learning approaches can enhance evidence-based practice capabilities among clinical nurses and should be integrated into continuing education programs to improve quality of care in clinical settings.

CIN Computers Informatics Nursing
Florence Nightingale Foundation (GB), Istanbul Florence Nightingale Hospital (TR), Istanbul University-Cerrahpaşa (TR), Istanbul Eye Hospital (TR), Nightingale Hospital (IN)
Quality Education
Openalex Percentile: Top 98%
Innovative Teaching Methods
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Effects of Flipped, Web-Based, and Traditional Learning Models on Clinical Nurses’ Self-Directed Learning and Metacognition — Emine Akçin Şenyuva, Deniz Yildirim · CIN Computers Informatics Nursing (2026) | TGRS Research Map | TGRS