Education Commons^™

The Impact Of Race And Ethnicity On The Identification Process For Giftedness In Utah, Russell Warne, Braydon Anderson, Alyce Johnson

Russell T Warne

Many gifted education experts have found that Black, Hispanic, and Native American students are less likely to be identified for gifted programs than Asian American and White students. A study was conducted to ascertain the degree of underrepresentation of these groups in gifted programs in Utah. Using state-collected data from 14,781 students in six representative school districts in Utah, it was found through multiple logistic regression analysis that there was no statistically significant difference in the likelihoods that Black, Hispanic, or Native American students and White students would be identified as gifted; Asian American and Pacific Islander students were more …

Learning And Fearing Mathematics, Sarah Buckley, Kate Reid

Dr Sarah Buckley

Findings from psychology and neuroscience can help educators to better understand the processes underlying children’s learning of, and feelings towards, mathematics. Sarah Buckley and Kate Reid explain.

Session M - From Experimental Psychology To A Science Of Learning, Ottmar Lipp, Sacha Develle

Dr Sacha DeVelle

Concurrent Session Block 3

Towards A Science Of Learning, Sacha Develle

Dr Sacha DeVelle

Recent discoveries in neuroscience, psychology and education have raised new questions about how learning takes place, emphasising the need for inter-disciplinary collaboration for a new ‘science of learning’, as Sacha DeVelle explains.

Young Children's Mental Health Outcomes: Flinders University Evaluation Of An Australia-Wide 2-Year Mental Health Promotion Initiative In Early Childhood And Care Settings, Helen Askell-Williams, Rosalind Murray-Harvey, Phillip Slee, Katherine Dix, Grace Skrzypiec

Dr Katherine Dix

The Impact Of Varied Discrimination Parameters On Mixed-Format Item Response Theory Model Selection, Tiffany Whittaker, Wanchen Chang, Barbara Dodd

Wanchen Chang

Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low variance of the discrimination parameters used to generate the data. This simulation study reproduced the Whittaker et al. study by incorporating more variability in the discrimination parameter estimates used to generate the data. The results indicated that the majority of the criteria performed more accurately when …