Aims: The aim of this study was to explore the relationship between the application of learning strategies and the emergence of higher-order learning behaviours among medical students in Chinese provincial undergraduate colleges, while also examining the impact of social demographic variables on the development of higher-order learning behaviours and learning strategy preferences.
Methodology: We conducted a relevant cross-sectional study using the Chinese College Student Survey (CCSS) online questionnaire to evaluate higher-order learning behaviours and learning strategies in medical undergraduate students attending provincial colleges in China. A total of 992 valid questionnaires were collected and analysed using SPSS 22.0 (SPSS Inc., Chicago, IL). We performed statistical analysis using one-sample -tests to compare the results with the national norm score for medical subjects in undergraduate colleges. We also conducted variance analysis and regression analysis.
Results: The study found that the average scores for higher-order learning behaviours, enquiry-based learning and receptive learning behaviour among medical undergraduate students in provincial colleges were higher than the national norm score for medical subjects, indicating a positive trend. However, the average scores for other indicators were lower than the national norm score. The utilization of learning strategies and the development of higher-order learning behaviours among students were affected by various factors such as grade and gender. The study suggests that the preference for certain learning strategies, such as enquiry-based, receptive, integrative and collaborative, can have a significant impact on the emergence of higher-order learning behaviours.
Conclusions: The study has demonstrated a positive correlation between the utilization of learning strategies and the development of higher-order learning behaviours. This relationship has been observed in medical students attending provincial undergraduate colleges, where the adoption of enquiry-based, receptive, integrative and collaborative learning strategies has been found to significantly influence the emergence of higher-order learning behaviours.KEY MESSAGESThe implementation of learning strategies among medical students in provincial undergraduate colleges in China has a significant impact on high-level learning behaviours.The impact of high-level learning behaviours is reliant on comprehensive support from four distinct learning strategies: receptive learning, inquiry-based learning, comprehensive learning and collaborative learning.One of the most impactful learning strategies is receptive learning, particularly on high-order learning behaviours. On the other hand, reflective learning does not seem to have a significant effect.Changes in grades can significantly impact higher-order learning behaviours and affect the propensity for reflective and collaborative learning strategies.Females generally exhibit a greater preference for receptive learning strategies, while males tend to exhibit a greater preference for inquiry-based learning strategies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184605 | PMC |
http://dx.doi.org/10.1080/07853890.2023.2205166 | DOI Listing |
Brief Bioinform
November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
View Article and Find Full Text PDFNat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Faculté des sciences infirmières, Université de Montréal, Succ. Centre-Ville, Montréal, C. P. 6128, H3C 3J7, Canada.
Background: Despite the importance of effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning, unanswered questions remain. Understanding how these cognitive operations can be observed and assessed is crucial, particularly considering the rapid growth of artificial intelligence and its integration into health education. A scoping review was conducted to map the literature regarding educational strategies to support transformation and articulation of clinical data, the learning tasks expected of students when exposed to these strategies and methods used to assess individuals' proficiency METHODS: Based on the Joanna Briggs Institute methodology, the authors searched 5 databases (CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science), ProQuest Dissertations & Theses electronic database and Google Scholar.
View Article and Find Full Text PDFOpen Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
Neurosciences (Riyadh)
January 2025
From the Department of Basic Medical Sciences, College of Medicine, Taibah University, Madinah, Kingdom of Saudi Arabia.
The hippocampus, noted as (HC), plays a crucial role in the processes of learning, memory formation, and spatial navigation. Recent research reveals that this brain region can undergo structural and functional changes due to environmental exposures, including stress, noise pollution, sleep deprivation, and microgravity. This review synthesizes findings from animal and human studies, emphasizing the HC's plasticity in response to these factors.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!