Sep 2026· Journal of Child Psychology and Psychiatry and Allied Disciplines· 0 citations· 18 references
Medicine
Abstract
Background
Early oral language skills are crucial for children's later academic and social development, yet systematic support for oral language development remains limited in Dutch primary education. The Nuffield Early Language Intervention (NELI) has proven effective in improving oral language skills among preschool children in the United Kingdom. The present study investigated whether the Dutch adaptation of NELI is similarly effective for Dutch preschoolers with relatively weak language skills in the language of instruction.
Methods
A total of 44 primary schools, 116 classrooms (58 intervention; 58 control), and 692 children aged 4 to 7 years participated in a cluster randomized controlled trial. Classrooms within schools were randomly assigned to intervention or control conditions. Within each classroom, five or six children with the lowest scores on a language screening measure were selected. Children in the intervention condition participated in the 20-week NELI program, receiving daily individual and small-group tutoring, while children in the control condition received education as usual. Before and after the intervention, children's oral language skills were individually assessed using standardized language measures.
Results
Structural equation modeling showed that children in the intervention condition made significantly greater improvements than those in the control condition on two latent language variables: lexical and syntactic skills (d = .15) and narrative skills (d = .19). Effects were not moderated by pre-test language scores, age, home language, sex, or parental education level.
Conclusions
The Dutch adaptation of NELI can effectively support the oral language development of preschoolers with relatively weak oral language skills. Although effect sizes were small, the findings indicate that NELI is a promising approach for supporting early oral language development in Dutch schools.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
Related blog posts
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.