In courses where topics are sensitive or even considered taboo for discussion, it can be difficult to assess students' deeper learning. In addition, incorporating a wide variety of students' values and beliefs, designing instructional strategies and including varied assessments adds to the difficulty. Journal entries or response notebooks can highlight reflection upon others' viewpoints, class readings, and additional materials. These are useful across all educational levels in deep learning and comprehension strategies assessments. Journaling meshes with transformative learning constructs, allowing for critical self-reflection essential to transformation. Qualitative analysis of journals in a death and dying class reveals three transformative themes: awareness of others, questioning, and comfort. Students' journal entries demonstrate transformative learning via communication with others through increased knowledge/exposure to others' experiences and comparing/contrasting others' personal beliefs with their own. Using transformative learning within gerontology and geriatrics education, as well as other disciplined aging-related courses is discussed.
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http://dx.doi.org/10.1080/02701960.2014.983499 | DOI Listing |
Anal Biochem
December 2024
Department of Biochemistry, Kampala International University-Western Campus, Ishaka, Uganda.
Aptamers, single-stranded nucleic acids that bind to specific targets with high affinity and specificity, hold significant promise in various biomedical and biotechnological applications. The traditional method of aptamer selection, SELEX (Systematic Evolution of Ligands by EXponential Enrichment) takes a lot of work and time. Recent advancements in computational methods have revolutionized aptamer design, offering efficient and effective alternatives.
View Article and Find Full Text PDFAcad Med
October 2024
T.H. Champney is professor, Department of Cell Biology, University of Miami Miller School of Medicine, Miami, Florida; ORCID: https://orcid.org/0000-0002-0507-1663.
A new ethos of anatomy education goes beyond the learning of body parts in the traditional curriculum. In the traditional curriculum, the focus of only providing information on the structure of the human body left certain learning opportunities overlooked, marginalized, or dismissed as irrelevant; thus, opportunities to foster and shape professional attributes in health care learners were lost. Furthermore, changes in curricula structures and reductions in anatomy teaching hours have necessitated a transformation in how anatomy education is perceived and delivered.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
J Imaging
December 2024
Institut Pascal, CNRS, Clermont Auvergne INP, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.
Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g.
View Article and Find Full Text PDFBiosensors (Basel)
December 2024
Department of Smart Health Science and Technology, Kangwon National University, Chuncheon 24341, Republic of Korea.
Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making.
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