World general knowledge facts

I'm pleased to share my new pub with my blog readers.  The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants - Kevin S. McGrew, 2021  https://ift.tt/3GwHB7D The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants Kevin S. McGrew First Published  October 25, 2021 Research Article  https://doi.org/10.1177/08295735211054270 Article information  Abstract The  Cognitive-Affective-Motivation Model of Learning  (CAMML) is a proposed framework for integrating contemporary motivation, affective (Big 5 personality) and cognitive (CHC theory) constructs in the practice of school psychologists (SPs). The central tenet of this article is that SPs need to integrate motivation alongside affective and cognitive constructs vis-à-vis an updated trilogy-of-the-mind (cognitive, conative, affective) model of intellectual functioning. CAMML builds on Richard Snow's s...

 Relations among phonological processing skills and mathematics in children: A meta-analysis. - PsycNET  https://ift.tt/3GAKDHW Citation Yang, X., Yan, M., Ruan, Y., Ku, S. Y. Y., Lo, J. C. M., Peng, P., & McBride, C. (2021). Relations among phonological processing skills and mathematics in children: A meta-analysis.  Journal of Educational Psychology.  Advance online publication.  https://doi.org/10.1037/edu0000710 Abstract The present study presents a meta-analysis of the relations between phonological processing abilities and different mathematics subskills. Using a random-effects model with 94 studies (135 unique samples, 826 effect sizes), the present meta-analysis revealed a significant general association between phonological processing and mathematics (average  r  = .33,  p  < .001, 95% CI [.30, .36]). Phonological awareness (PA) and rapid automatized naming (RAN) showed stronger correlations with mathematics than phonolo...

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting | bioRxiv  https://ift.tt/3jBCPvt Abstract Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSF...

 'Race-norming' kept former NFL players from dementia diagnoses. Their families want answers.  https://ift.tt/3CYfQ5e I believe this refers to demographically adjusted or Heaton neuropsych norms…which have occasionally been used inappropriately in Atkins ID death penalty cases. ****************************************** Kevin S. McGrew, PhD Educational & School Psychologist Director Institute for Applied Psychometrics (IAP) https://www.themindhub.com ****************************************** from IQ's Corner https://ift.tt/3BacV8Q via IFTTT https://ift.tt/3itegQY