Although many interventions have generated immediate positive effects on mathematics achievement, these effects often diminish over time, leading to the important question of what causes fadeout and persistence of intervention effects. This study investigates how children's forgetting contributes to fadeout and how transfer contributes to the persistence of effects of early childhood mathematics interventions. We also test whether having a sustaining classroom environment following an intervention helps mitigate forgetting and promotes new learning. Students who received the intervention we studied forgot more in the following year than students who did not, but forgetting accounted for only about one-quarter of the fadeout effect. An offsetting but small and statistically non-significant transfer effect accounted for some of the persistence of the intervention effect - approximately one-tenth of the end-of-program treatment effect and a quarter of the treatment effect one year later. These findings suggest that most of the fadeout was attributable to control-group students catching up to the treatment-group students in the year following the intervention. Finding ways to facilitate more transfer of learning in subsequent schooling could improve the persistence of early intervention effects.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6541454PMC
http://dx.doi.org/10.1037/edu0000297DOI Listing

Publication Analysis

Top Keywords

transfer learning
8
early childhood
8
childhood mathematics
8
mathematics interventions
8
persistence intervention
8
intervention effects
8
intervention
6
persistence
5
fadeout
5
effects
5

Similar Publications

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Due to the lack of symptoms until advanced stages, early diagnosis of ccRCC is challenging. Therefore, the identification of novel secreted biomarkers for the early detection of ccRCC is urgently needed.

View Article and Find Full Text PDF

HIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.

J Chem Inf Model

January 2025

State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China.

The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy by disrupting interactions with inhibitors. Traditional methods, such as biochemical assays and structural biology, are crucial for studying enzyme function but are time-consuming and labor-intensive.

View Article and Find Full Text PDF

Severity of metabolic derangement predicts survival after out-of-hospital cardiac arrest and the likelihood of benefiting from extracorporeal life support.

Emergencias

December 2024

Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seúl, República de Corea. Department of Digital Health, SAIHST, Sungkyunkwan University, Seúl, República de Corea.

Objective: To develop a Metabolic Derangement Score (MDS) based on parameters available after initial testing and assess the score's ability to predict survival after out-of hospital cardiac arrest (OHCA) and the likely usefulness of extracorporeal life support (ECLS).

Methods: A total of 5100 cases in the Korean Cardiac Arrest Research Consortium registry were included. Patients' mean age was 67 years, and 69% were men.

View Article and Find Full Text PDF

Background: Diabetic kidney disease (DKD) is one of the typical complications of type 2 diabetes (T2D), with approximately 10 % of DKD patients experiencing a Rapid decline (RD) in kidney function. RD leads to an increased risk of poor outcomes such as the need for dialysis. Albuminuria is a known kidney damage biomarker for DKD, yet RD cases do not always show changes in albuminuria, and the exact mechanism of RD remains unclear.

View Article and Find Full Text PDF

Background/purpose: Oral squamous cell carcinoma (OSCC) is notorious for its low survival rates, due to the advanced stage at which it is commonly diagnosed. To enhance early detection and improve prognostic assessments, our study harnesses the power of machine learning (ML) to dissect and interpret complex patterns within mRNA-sequencing (RNA-seq) data and clinical-histopathological features.

Materials And Methods: 206 retrospective Vietnamese OSCC formalin-fixed paraffin-embedded (FFPE) tumor samples, of which 101 were subjected to RNA-seq for classification based on gene expression.

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!