Little is known about the development of number knowledge (NK) and the antecedents of low-persistent NK profiles in early childhood. We documented the developmental trajectories of NK across the transition from preschool to elementary school, their predictive validity with respect to later math achievement, and the child and family early-life factors associated with low NK profiles. Children's NK was assessed four times at regular intervals between the ages 4 and 7 years in a large, representative population-based sample. Developmental trajectories of NK were established for 1597 children. These children were also assessed with respect to several features of their family environment at 5, 17, and 29 months, as well as their cognitive skills at age 41 months. Analyses revealed a best-fitting 4-trajectory model, characterized by Low-Increasing (10% of the children), Moderate-Increasing (39%), Moderate-Fast Increasing (32%) and High-Increasing (19%) groups. Children of these trajectory groups differed significantly with respect to math achievement at ages 8 and 10 years, with the Low-Increasing group persistently scoring lower than the other groups throughout these years. Children of Low-Increasing NK group were from household of lower income and father with low educational background, poorer early cognitive development, and more importantly, reduced visual-spatial skills and memory-span. Children displaying reduced cognitive abilities and impoverished living conditions early in life are at greater risk of low NK throughout late preschool and school entry, with ensuing difficulties in math achievement. They deserve early preventive attention to help alleviate later mathematic difficulties.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jsp.2018.02.004DOI Listing

Publication Analysis

Top Keywords

math achievement
16
developmental trajectories
12
number knowledge
8
respect math
8
low-increasing group
8
children
6
early
5
early developmental
4
trajectories number
4
math
4

Similar Publications

Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.

View Article and Find Full Text PDF

Peste des Petits Ruminants (PPR) is a highly contagious transboundary viral disease of small ruminants with significant economic implications caused by the Peste des Petits Ruminants virus. This study employs mathematical modeling to investigate the impact of imperfect PPR vaccines and restocked small ruminants on the transmission dynamics of PPR. A deterministic mathematical model is developed by incorporating vaccinated and restocked subpopulations into the classical SEIR model.

View Article and Find Full Text PDF

Investigation of Filtration Performances in Eggshell Ultrafiltration Membranes with Surface Functionalized Using Graphene Oxide.

ACS Omega

December 2024

Department of Physics, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km 21 Jatinangor, Sumedang 45363, Indonesia.

Efforts to prevent fouling are crucial in advancing ultrafiltration (UF) membranes, especially in addressing the concentration polarization of the accumulation of dissolved dye molecules in wastewater. This study explores the impact of incorporating graphene oxide (GO) onto eggshell (ES) UF membranes regarding their permeability, rejection efficiency, and permeate flow rate. The ES-GO membranes were obtained from eggshells that were modified with varied concentrations of GO (0.

View Article and Find Full Text PDF

Background And Purpose: Endovascular thrombectomy (EVT) for acute ischemic stroke (AIS) with M2 segment occlusion of the middle cerebral artery (MCA) is debatable. This study assessed the efficacy, safety, and functional outcomes of EVT in M2 occlusion patients, examining differences in outcomes based on the dominance of the occluded segment (DomM2 vs. Non-DomM2).

View Article and Find Full Text PDF

Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!