Unlabelled: One promising practice for increasing active learning in undergraduate science education is the use of a mentoring network. The Promoting Active Learning and Mentoring (PALM) Network was launched with practitioners from several professional societies and disciplines to make changes in their teaching based on evidence-based practices and to encourage the members to reflect deeply on their teaching experiences. Members of the Network interviewed seven previous Fellows, 1 to 6 years after completing their fellowship, to better understand the value of the Network and how these interactions impacted their ability to sustain change toward more active teaching practices. The interviews resulted in the creation of three personas that reflect the kinds of educators who engaged with the Network: Neil the Novice, Issa the Isolated, and Etta the Expert. Key themes emerged from the interviews about how interactions with the PALM Network sustained change toward evidence-based teaching practices allowing the members to readily adapt to the online learning environment during the COVID-19 pandemic. Understanding how the personas intersect with the ADKAR model contributes to a better understanding of how mentoring networks facilitate transformative change toward active learning and can inform additional professional development programs.
Supplementary Information: The online version contains supplementary material available at 10.1007/s44217-022-00023-w.
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http://dx.doi.org/10.1007/s44217-022-00023-w | DOI Listing |
Front Chem
December 2024
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, and . While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.
View Article and Find Full Text PDFAnat Sci Educ
January 2025
Department of Anatomy, Cell Biology, & Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Active recall, the act of recalling knowledge from memory, and games-based learning, the use of games and game elements for learning, are well-established as effective strategies for learning gross anatomy. An activity that applies both principles is Catch-Phrase, a fast-paced word guessing game. In Anatomy Catch-Phrase, players must get their teammates to identify an anatomical term by describing its features, functions, or relationships without saying the term itself.
View Article and Find Full Text PDFRev Paul Pediatr
December 2024
Universidade Federal de Pernambuco, Recife, PE, Brazil.
Objective: To verify the level of knowledge of Brazilian pediatricians about anaphylaxis, identifying sociodemographic and educational characteristics of the professional which contribute to the adequate management of this clinical disorder.
Methods: A survey was carried out on the management of anaphylaxis using a questionnaire prepared and distributed by email to pediatricians in different states in Brazil. The level of knowledge about anaphylaxis was classified as: satisfactory; unsatisfactory; more than satisfactory; ideal, according to evaluation criteria adopted for the statements of clinical cases that addressed the drug of choice, route of administration, positioning of the patient with anaphylaxis and recognition of the clinical case with differential diagnosis.
PLoS One
January 2025
School of Foundation Courses, Chongqing Institute of Engineering, Chongqing, China.
Link prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduces a novel algorithm named SW-Metapath2vec. This algorithm enhances the embedding learning process by assigning weights to meta-path traces generated through random walks and translates the potential connections between nodes into the cosine similarity of embedded vectors.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Microsoft Research, Cambridge, Massachusetts, United States of America.
Machine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods (Bayesian optimization and active learning) require calibrated estimations of model uncertainty. While studies have benchmarked a variety of deep learning uncertainty quantification (UQ) methods on standard and molecular machine-learning datasets, it is not clear if these results extend to protein datasets.
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