During the COVID-19 pandemic, many parents suddenly had to assume responsibility for their children's learning at home. Research conducted before the pandemic showed that mathematics homework is often unsuccessful or stressful for both parents and children and that tension exists between home and school in the learning of mathematics. Understanding parents' experience of home-learning mathematics during lockdown has implications for positive learning relationships between home and school in the future. During the lockdown, we sent an online survey to New Zealand parents and received 634 responses. We found that parents were generally very engaged in the home learning of mathematics. They reported a range of opinions about the quality of mathematics work and teacher support, and there was a correlation between general stress levels and negative opinions. To further support their child's mathematics learning, many parents turned to online mathematics programs, about which they were very positive. Parents of younger children were more positive about their overall home-learning experiences of mathematics, but the crisis brought to the fore several pre-existing issues. We argue that these findings have implications for mathematics home learning in the future; we suggest that schools listen to parental feedback regarding the quality, level, and quantity of mathematics work. Additionally, schools could consider ways to deliver effective teacher support and to foster parental agency in helping their children with mathematics learning.
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http://dx.doi.org/10.1007/s10763-021-10222-w | DOI Listing |
JMIR Aging
January 2025
Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos SP, Brazil.
Background: The prevalence of stroke is high in both males and females, and it rises with age. Stroke often leads to sensor and motor issues, such as hemiparesis affecting one side of the body. Poststroke patients require torso stabilization exercises, but maintaining proper posture can be challenging due to their condition.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Internal Medicine, Hospital Clinic, Institut d'Investigacio Biomèdica August Pi i Sunyer, Barcelona, Spain.
Background: Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.
Objective: This study evaluates whether the Greenhabit (mobile health [mHealth]) behavioral treatment enhances T2D outcomes compared with standard care.
Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.
PLOS Digit Health
January 2025
Department of Mathematics & Statistics, York University, Toronto, Canada.
Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk.
View Article and Find Full Text PDFPLoS One
January 2025
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, Egypt.
The Weibull distribution is an important continuous distribution that is cardinal in reliability analysis and lifetime modeling. On the other hand, it has several limitations for practical applications, such as modeling lifetime scenarios with non-monotonic failure rates. However, accurate modeling of non-monotonic failure rates is essential for achieving more accurate predictions, better risk management, and informed decision-making in various domains where reliability and longevity are critical factors.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings.
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