Objective: To evaluate the students' experience with problem-based learning.
Methods: This cross-sectional, qualitative study was conducted at the College of Medicine, Al Jouf University, Sakakah, Saudi Arabia, in October 2015, and comprised medical students of the 1st to 5th levels. Interviews were conducted using Students' Course Experience Questionnaire. The questionnaire contained 37 questions covering six evaluative categories: appropriate assessment, appropriate workload, clear goals and standards, generic skills, good teaching, and overall satisfaction. The questionnaire follows the Likert's scale model. Mean values were interpreted as: >2.5= at least disagree, 2.5->3= neither/nor (uncertain), and 3 or more= at least agree.
Results: Of the 170 respondents, 72(42.7%) agreed that there was an appropriate assessment accompanied with the problem-based learning. Also, 107(63.13%) students agreed that there was a heavy workload on them. The goal and standards of the course were clear for 71(42.35%) students, 104(61.3%) agreed that problem-based learning improved their generic skills, 65(38.07%) agreed the teaching was good and 82(48.08%) students showed overall satisfaction.
Conclusions: The students were satisfied with their experience with the problem-based learning.
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Clin Teach
February 2025
UWA Dental School, The University of Western Australia, Nedlands, Western Australia, Australia.
Community-based dental education (CBDE) is essential for equipping dental students with the practical skills required for independent practice while simultaneously addressing the oral health needs of the community through real-world experiential learning. The success of CBDE initiatives rely on effective collaboration across stakeholders, including educational institutions, community organisations, and students, to address both faculty educational goals and community oral health needs. This paper introduces a practical toolbox to support CBDE program implementation.
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Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, EH9 3FB, UK.
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School of Computer Science and Technology, Soochow University, Suzhou, 215006, China. Electronic address:
Certifying robustness against external uncertainties throughout the control process to reduce the risk of instability is very important. Most existing approaches based on adversarial learning use a fixed parameter to adjust the intensity of adversarial perturbations and design these perturbations in a greedy manner without considering future implications. However, they often lead to severe vulnerabilities when attack budgets vary dynamically or under foresighted attacks.
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Motor Control and Learning Group, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent.
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