Objective: This study aimed to develop and implement a program-wide active learning framework to guide active learning and assessment efforts in an entry-to-practice competency-based Doctor of Pharmacy program.
Methods: The development of the framework involved 3 stages: creation of a framework aligned with the program's guiding principles, provision of training and support to faculty and students, and evaluation of the students' and academic staff satisfaction using an online survey over 2 academic years (2022-2023). Data from this survey were analyzed descriptively.
Results: An active learning framework that was aligned with the program's guiding principles while allowing flexibility for individual teaching styles was developed. It consisted of 4 stages: preclass preparation, in-class work, prelaboratory preparation, and in-laboratory activities (emphasizing knowledge acquisition and competency development). Academic staff surveys reported higher satisfaction of staff in year 2 than year 3 of the program, with indications of further training on specific modalities. Students' satisfaction improved from year 2 to 3, particularly, in areas related to class objectives, learning environment, and feedback.
Conclusion: The transformation of a curriculum that includes the evolution of the teaching and learning strategy is a complex, long-term project that deserves continuing attention. Having frameworks in place helps the management, instructors, and students to understand the global direction, stay focused, and support the implementation of competency-based education and student-centered learning.
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http://dx.doi.org/10.1016/j.ajpe.2024.101272 | DOI Listing |
Viruses
November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.
View Article and Find Full Text PDFSensors (Basel)
December 2024
South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effective training dataset with data of low similarity to the existing one. A detailed explanation of the principle of the proposed methods is given.
View Article and Find Full Text PDFBiomedicines
December 2024
Department of Psychiatry, Division of Molecular Therapeutics, New York State Psychiatric Institute, Columbia University, New York, NY 10032, USA.
Background/objectives: Learning is classically modeled to consist of an acquisition period followed by a mastery period when the skill no longer requires conscious control and becomes automatic. Dopamine neurons projecting to the ventral striatum (VS) produce a teaching signal that shifts from responding to rewarding or aversive events to anticipating cues, thus facilitating learning. However, the role of the dopamine-receptive neurons in the ventral striatum, particularly in encoding decision-making processes, remains less understood.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters.
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