Background: Research on individual learning approaches (or learning styles) is split in two traditions, one of which is biased towards academic learning, and the other towards learning from direct experience.
Aims: In the reported study, the two traditions are linked by investigating the relationships between school-based (academic) and work-based (experiential) learning approaches of students in vocational education programs.
Samples: Participants were 899 students of a Dutch school for secondary vocational education; 758 provided data on school-based learning, and 407 provided data on work-based learning, resulting in an overlap of 266 students from whom data were obtained on learning in both settings.
Methods: Learning approaches in school and work settings were measured with questionnaires. Using factor analysis and cluster analysis, items and students were grouped, both with respect to school- and work-based learning.
Results: The study identified two academic learning dimensions (constructive learning and reproductive learning), and three experiential learning dimensions (analysis, initiative, and immersion). Construction and analysis were correlated positively, and reproduction and initiative negatively. Cluster analysis resulted in the identification of three school-based learning orientations and three work-based learning orientations. The relation between the two types of learning orientations, expressed in Cramér's V, appeared to be weak.
Conclusions: It is concluded that learning approaches are relatively context specific, which implies that neither theoretical tradition can claim general applicability.
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http://dx.doi.org/10.1348/000709905X42932 | DOI Listing |
Trials
January 2025
MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, 90 High Holborn, London, WC1V 6LJ, UK.
Need For A Strategic Approach To Knowledge Transfer And Exchange: Late-phase clinical trials and systematic reviews find results that have the potential to improve health outcomes for people. However, there are often delays in these results influencing clinical practice. We developed a knowledge transfer and exchange strategy to support research teams, aiming to identify activities along the research process to maximise and accelerate the research impact.
View Article and Find Full Text PDFBiomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
BMC Health Serv Res
January 2025
Department of Development Studies, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
Background: Over the years, the processing of research proposals for ethical approvals have been done manually through a review of hard copies. Longer turn-around-time, increased financial costs to researchers and cumbersome submission processes are few of the challenges inherent to paper-based review of research proposals. This has necessitated the shift to electronic management of research proposals, Research Ethics Information Management Systems (REIMS).
View Article and Find Full Text PDFThe increasing prevalence of diabetes mellitus worldwide necessitates that medical undergraduates acquire a deep understanding of the disease to ensure accurate diagnosis and effective management. Traditional teaching methods, while foundational, often lack the interactive elements that enhance student engagement and knowledge retention. This study aimed to evaluate the effectiveness of a novel educational board game, "Diabe-teach," in enhancing knowledge retention among medical students compared with conventional self-study methods.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
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