Learning in groups is a common feature of science classrooms. The three articles I have chosen to feature in this installment of reflect recent research of group learning at different scales. The first examines within-group dynamics, identifying interactions among students that allow scientific sense-making discussions to begin and continue. The second proposes to study groups as the unit of analysis, asking why some groups are able to persevere in the face of challenging problems. The third considers the potential for learning to occur between groups, through connections in students' extended social networks. Each brings new ideas and questions to the study of group learning.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755208 | PMC |
http://dx.doi.org/10.1187/cbe.19-03-0067 | DOI Listing |
HGG Adv
January 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
Genetics plays a significant role in Multiple Sclerosis (MS), with approximately 12.6% of cases occurring in familial form. While previous studies have demonstrated differences in disease progression and MRI findings between familial and sporadic MS, there has been no comparison of cognitive impairment between them.
View Article and Find Full Text PDFSci Rep
January 2025
School of Physics, Engineering and Technology, University of York, Heslington, York, YO10 5DD, UK.
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Introduction: Ultrasound is important in heart diagnostics, yet implementing effective cardiac ultrasound requires training. While current strategies incorporate digital learning and ultrasound simulators, the effectiveness of these simulators for learning remains uncertain. This study evaluates the effectiveness of simulator-based versus human-based training in Focused Assessed with Transthoracic Echocardiography (FATE).
View Article and Find Full Text PDFBMC Med Educ
January 2025
University of Birmingham, Birmingham, United Kingdom.
Purpose: Free Open Access Medical Education (FOAMed) is an emergent phenomenon within medical education. The rise of FOAMed resources has meant that medical education needs no longer be confined to the lecture theatre or the hospital setting, but rather, can be produced and shared amongst any individual or group with access to internet and a suitable device. This study presents a review of the use of FOAMed resources by students as part of their university medical education.
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