Front Psychol
Department for Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Tübingen, Tübingen, Germany.
Published: October 2020
Self-regulation is a multidimensional construct that is positively related to academic achievement, such as successful mathematics performance. However, this relation of self-regulation and mathematics performance has mainly been investigated in Western countries with similar cultural contexts, although self-regulation is assumed to be context-sensitive. Therefore, the present study investigated the relation of self-regulation and mathematics performance across two different countries (Germany vs. Iran) in college students. The relation of self-regulation and mathematics performance was expected to be weaker in students of math-related fields, such as Engineering/Informatics, as they are assumed to need less self-regulation to solve the mathematics problems than students of less math-related fields, such as Human Sciences. In total, 122 undergraduate students (German = 60; Iranian = 62) of Human Sciences or Engineering/Informatics participated in this study. We measured self-regulation with the Brief Self-Control Scale (Tangney et al., 2004) and mathematics performance with a complex multiplication test. Results showed that self-regulation did not predict multiplication performance in German or Iranian students, in general. However, when the field of study was considered, self-regulation predicted multiplication performance in the subgroup of German and Iranian students studying Human Sciences within each country. We conclude that cultural context does not seem to play a dominant role in moderating the relation between self-regulation and math performance, however, field of study and more generally familiarity with math may be an important factor to consider in single or cross-cultural studies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661690 | PMC |
http://dx.doi.org/10.3389/fpsyg.2020.489371 | DOI Listing |
PLoS One
January 2025
Division of Gastroenterology & Hepatology, University of Toronto, Toronto, ON, Canada.
Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Mathematics, Aswan University, Faculty of Science, Aswan, Egypt.
In this work, bridge network model with Rayleigh distribution lifetimes is used. Two main techniques are calculated to upgrade this model: reduction and redundancy techniques. In order to compare the effectiveness of the various approaches, the survival function, the mean time to failure and gamma-fractiles for the original and upgraded model are calculated.
View Article and Find Full Text PDFPeerJ
January 2025
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.
Background: Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome-wide association studies. Existing studies converting the data into grayscale images have shown promising.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Introduction: Like many other academic medical centers, the University of Alabama at Birmingham (UAB) aspires to adopt learning health system (LHS) principles and practices more fully. Applying LHS principles establishes a culture where clinical and operational practices constantly generate questions and leverage information technology (IT) and methodological expertise to facilitate systematic evaluation of care delivery, health outcomes, and the effects of improvement initiatives. Despite the potential benefits, differences in priorities, timelines, and expectations spanning an academic medical center's clinical care, administrative operations, and research arms create barriers to adopting and implementing an LHS.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The field of medical image segmentation powered by deep learning has recently received substantial attention, with a significant focus on developing novel architectures and designing effective loss functions. Traditional loss functions, such as Dice loss and Cross-Entropy loss, predominantly rely on global metrics to compare predictions with labels. However, these global measures often struggle to address challenges such as occlusion and nonuni-form intensity.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!
© LitMetric 2025. All rights reserved.