In the present study, we investigated three factors that were assumed to have a significant influence on the success of learning from multiple hypertexts, and on the construction of a documents model in particular. These factors were task (argumentative vs. narrative), available text material (with vs. without primary sources), and presentation format (active vs. static). The study was conducted with the help of the combination of three tools (DEWEX, Chemnitz LogAnalyzer, and SummTool) developed for Web-based experimenting. The results show that the task is the most important factor for successful learning from multiple hypertexts. Depending on the task, the participants were either able or unable to apply adequate strategies, such as considering the source information. It was also observed that argumentative tasks were supported by an active hypertext presentation format, whereas performance on narrative tasks increased with a passive presentation format. No effect was shown for the type of texts available.
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http://dx.doi.org/10.3758/BRM.41.3.639 | DOI Listing |
PLoS Biol
January 2025
Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota, United States of America.
Worrying about perceived threats is a hallmark of multiple psychological disorders including anxiety. This concern about future events is particularly important when an individual is faced with an approach-avoidance conflict. Potential goals to approach are known to be represented in the dorsal hippocampus during theta cycles.
View Article and Find Full Text PDFJ Neuroophthalmol
December 2024
Division of Ophthalmology (EB-S, AS, AA-A, AS-B, DW, SS, FC), Department of Surgery, University of Calgary, Calgary, Canada; Department of Biomedical Engineering (CN), University of Calgary, Calgary, Canada; Departments of Neurology (LBDL) and Ophthalmology (LBDL), University of Michigan, Ann Arbor, Michigan; and Department of Clinical Neurosciences (SS, FC), University of Calgary, Calgary, Canada.
Background: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss.
View Article and Find Full Text PDFJ Gen Intern Med
January 2025
Department of Medicine, Division of General Internal Medicine, Section of Hospital Medicine, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, USA.
Background: Medicine sub-internships aim to prepare students for residency. However, the traditional sub-internship structure, with multiple learners at varied levels, poses obstacles to providing the clinical exposure, learning environment, and direct observation and feedback necessary to develop essential skills.
Aim: Investigate the educational experience of learners on a coaching-centered sub-internship (CCSI) on a resident uncovered ward service.
Med Biol Eng Comput
January 2025
Non-Invasive Imaging and Diagnostic Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India.
Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.
We present an algorithm that combines quantum scattering calculations with probabilistic machine-learning models to predict quantum dynamics rate coefficients for a large number of state-to-state transitions in molecule-molecule collisions much faster than with direct solutions of the Schrödinger equation. By utilizing the predictive power of Gaussian process regression with kernels, optimized to make accurate predictions outside of the input parameter space, the present strategy reduces the computational cost by about 75%, with an accuracy within 5%. Our method uses temperature dependences of rate coefficients for transitions from the isolated states of initial rotational angular momentum j, determined via explicit calculations, to predict the temperature dependences of rate coefficients for other values of j.
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