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This study aims to evaluate the effectiveness of a competency-based learning (CBL) approach to an e-learning course on systems analysis and design (SAD). The competency of 18 students who registered for an SAD course was measured at different 3 times during the semester with the use of a competency diary. The changes in the competency scores through the semester were analyzed by a Friedman test, and the factors affecting learning effectiveness were identified by multiple regression. The competency scores increased as the semester progressed. The factors that had a significant effect on learning effectiveness were course management and learning materials. The authors found that the CBL approach worked well for this particular e-learning course on SAD and that nontechnical aspects of the instruction, such as course management and lecture materials, were more important than the technical aspects even in this Internet environment.
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http://dx.doi.org/10.1177/1010539509357931 | DOI Listing |
Cureus
August 2025
Physiology, SGT University, Gurugram, IND.
Introduction Simulation-based training has been a vital part of medical education since Competency-Based Medical Education (CBME) was introduced, and new guidelines since 2023 have expanded to include simulation as a mandatory methodology of teaching. This method enables learners to build and develop both technical and non-technical abilities in a safe and controlled setting, enhancing their preparedness for real-life medical scenarios. Simulation-based training improves skill acquisition and retention and enhances learners' confidence, reduces anxiety, reinforces learning, corrects errors, and promotes reflective practice, in contrast with the traditional method of teaching.
View Article and Find Full Text PDFJ Nurs Educ
September 2025
School of Nursing, Concordia University, Mequon, Wisconsin.
Background: The shift to competency-based education (CBE) creates a need to examine methods of teaching and evaluating physical health assessment competencies in entry-level and advanced-level nursing courses.
Method: A national survey, guided by backward design, gathered data on behaviors indicative of physical assessment competency, assessment strategies, and teaching and learning approaches that foster competency development.
Results: Respondents from 54 entry-level and 27 advanced-level programs completed the survey.
J Med Educ Curric Dev
September 2025
Department of General Pediatrics, Pediatric Cardiology and Neonatology, Medical Faculty, University Children's Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
Background: Medical education has been experiencing a transition from time- to competency-based. Since their introduction by Olle ten Cate in 2005, entrustable professional activities are a part of this process. We implemented a set of EPAs for the first 3 years of training at our hospital, encompassed by informational materials for trainees and supervisors.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
School of Nursing, Anhui University of Chinese Medicine, Hefei, China.
Purpose: This study evaluates the effectiveness of integrating case-based mind maps and reflective journals within Kolb's experiential learning framework in advanced nursing education.
Methods: An design compared 2023 (control group, = 46) and 2024 (experimental group, = 57) cohorts of nursing master's students. The experimental group received a Kolb-based intervention comprising: case analysis (concrete experience), reflective journals (reflective observation), mind maps (abstract conceptualization), and peer-led simulations (active experimentation).
Adv Med Educ Pract
August 2025
Department of Public Health, Faculty of Medicine, Padjadjaran University, Bandung, West Java, Indonesia.
Background: Currently, midwifery education is confronted with a variety of obstacles, such as inadequate resources and conventional learning methods that are less effective in enhancing the clinical skills of students. Technological advancements and the rapid evolution of maternal and neonatal health services necessitate the transformation of midwifery education to a competency-based curriculum and outcome-based assessment paradigm. Artificial intelligence (AI) and deep learning have the potential to provide adaptive, personalized, and precise learning in this context.
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