Introduction: This study examined student access to online resources of a faculty's learning management system (LMS). Issues relating to current e-learning resources usage were identified and formed the basis for recommendations to help assist stakeholders in teaching, learning and research.
Methods: Learning analytics from four cohorts of undergraduate dental students were extracted from the database of a LMS spanning between 2012 and 2016. Individual datasets were combined into one master file, re-categorised, filtered and analysed based on cohort, year of study, course and nature of online resource.
Results: A total of 157,293 access events were documented. The proportion of administrative to learning data varied across cohorts, with oldest cohort having the highest ratio (82:18) in their final year and most recent cohort having a ratio of 33:67 in their 4th year demonstrating a higher proportion to learning. Seven Learning domains were identified in the access data: access to problem-based learning resources was the highest and next was fixed prosthodontics videos. The prosthodontics discipline had the highest access across the curriculum while some others had very limited or even no learning access events.
Conclusion: A number of limitations have been identified with the analytics and learning resources in this LMS and engagement with learning resource provision. More detailed data capture of access use and unique identifiers to resources as well as keyword tagging of the resources are required to allow accurate mapping and support of students learning. Moreover, motivation or nudging of students behaviour to more actively engage with learning content needs exploration.
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http://dx.doi.org/10.1111/eje.12664 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Nursing Management, Florence Nightingale Faculty of Nursing, Istanbul University-Cerrahpaşa, Istanbul, Türkiye.
Purpose: This research aimed to determine the relationship between work intensification and occupational fatigue in nurses using a cross-sectional and correlational design.
Methods: The sample included 597 nurses from public, private, and university hospitals in Istanbul, selected through convenience sampling. Data were collected using the "Nurse Information Form," the "Intensification of Job Demands Scale," and the "Occupational Fatigue Exhaustion/Recovery Scale.
BMC Pharmacol Toxicol
January 2025
Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264100, PR China.
Background: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a critical but underexplored aspect of AD pathophysiology, representing a significant gap in our current understanding of the disease.
Methods: To investigate the involvement of GlnMgs in AD, we conducted a comprehensive bioinformatic analysis.
J Cheminform
January 2025
Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, Republic of Korea.
The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have been developed to predict hERG blockers, which can efficiently save time and resources.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
J Transl Med
January 2025
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
Background: Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.
Methods: We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies.
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