Oct 2026· Frontiers in Psychology· 0 citations· 117 references
Physical Education and Pedagogy
Abstract
Physical education (PE) offers opportunities for psychological learning, but evidence of benefit depends on the outcome, design, and instructional context.
This systematic review and multilevel meta-analysis included 74 reports from 70 trial families of primary-school PE interventions. Four databases covered English-language reports published from January 2000 through 31 May 2026. Randomized and nonrandomized controlled studies were distinguished from uncontrolled pre -post and short-term within-child designs. Multilevel models addressed dependent effects, with trial-family CR2 inference.
Source-informed controlled analyses contained five core social-emotional effects from three families (g = 0.947, 95% CI [−0.774, 2.667]; Holm-adjusted
p
= 0.424) and 13 adjacent psychological effects from five families (g = 0.215, 95% CI [−0.386, 0.817]; Holm-adjusted
p
= 0.424). Model-based prediction intervals were [−2.387, 4.280] and [−1.368, 1.799], respectively. The broader controlled analysis, requiring additional variance or clustering assumptions, included 126 effects from 28 families and yielded g = 0.381, 95% CI [0.135, 0.627].
The broader and combined source-informed estimates were similar (g = 0.381 and 0.421, respectively), although the latter was less precise; because the analyses overlap, they do not provide independent confirmation of benefit. The primary estimates remain too uncertain to define a dependable average benefit, rather than demonstrating that benefits are absent. Positive broader evidence warrants attention alongside substantial heterogeneity, source-reporting limitations, and an inconclusive exploratory small-study diagnostic. Future trials should align psychological targets, instructional content, implementation measures, and validated outcomes, and report allocation-aware uncertainty.
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
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.