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Direct linkHamilton, Rashea; Long, Daniel; McCoach, D. Betsy; Hemmler, Vonna; Siegle, Del; Newton, Sarah D.; Gubbins, E. Jean; Callahan, Carolyn M. – Journal for the Education of the Gifted, 2020
English learners (ELs) are the fastest growing population of students in the United States and currently represent nearly 10% of public school enrollment; however, they also constitute less than 3% of gifted program enrollment in these schools. Although an increasing number of studies explore this underrepresentation, research that specifically…
Descriptors: English Language Learners, Language Proficiency, Academically Gifted, Talent Identification
Rambo, Karen E.; McCoach, D. Betsy – Journal for the Education of the Gifted, 2012
Despite the research supporting acceleration, some teachers are still hesitant to recommend acceleration for advanced students. The Teacher Attitudes Toward Subject-Specific Acceleration (TATSSA) instrument was designed to uncover the factors that influence teacher decisions to recommend students for subject-specific acceleration. First, we…
Descriptors: Exceptional Child Research, Academically Gifted, Advanced Students, Gifted
Mun, Rachel U.; Hemmler, Vonna; Langley, Susan Dulong; Ware, Sharon; Gubbins, E. Jean; Callahan, Carolyn M.; McCoach, D. Betsy; Siegle, Del – Journal for the Education of the Gifted, 2020
Although the number of English learners (ELs) in the United States continues to increase, this population remains underserved by gifted and talented (GT) education programs across the nation. This underrepresentation represents a societal and research dilemma for reasons we address in this systematic review of the most effective practices…
Descriptors: English Language Learners, Academically Gifted, Talent Identification, Gifted Education
Gubbins, E. Jean; Siegle, Del; Peters, Pamela M.; Carpenter, Ashley Y.; Hamilton, Rashea; McCoach, D. Betsy; Puryear, Jeb S.; Langley, Susan Dulong; Long, Daniel – Journal for the Education of the Gifted, 2020
The underrepresentation of English learners (ELs) in gifted and talented programs is a societal and research problem that merits investigation. Three state departments of education and their state directors of gifted programs supported our access to 16 schools across nine districts. In these three states with gifted identification and programming…
Descriptors: Academically Gifted, Talent Identification, English Language Learners, Disproportionate Representation
Black, Anne C.; McCoach, D. Betsy – Journal for the Education of the Gifted, 2008
This article examines the psychometric properties of the 104-item Thinking Styles Inventory (TSI; Sternberg & Wagner, 1992) using responses from 789 students from 4 high schools in Connecticut. Twelve of the 13 subscales identified in mental self-government (MSG) theory (Sternberg, 1988, 1997) were included in all analyses. Both subscale- and…
Descriptors: Reliability, Factor Structure, Factor Analysis, Psychometrics
Siegle, Del; Gubbins, E. Jean; O'Rourke, Patricia; Langley, Susan Dulong; Mun, Rachel U.; Luria, Sarah R.; Little, Catherine A.; McCoach, D. Betsy; Knupp, Tawnya; Callahan, Carolyn M.; Plucker, Jonathan A. – Journal for the Education of the Gifted, 2016
Gifted students' learning gains result from complex, advanced, and meaningful content provided by a knowledgeable teacher through high-quality curriculum and instruction at an appropriate pace with scaffolding and feedback. These elements exert influence that increases with dosage and within structures that facilitate student engagement in…
Descriptors: Disadvantaged, Disproportionate Representation, Minority Groups, Social Bias
McCoach, D. Betsy – Journal for the Education of the Gifted, 2003
Structural equation modeling (SEM) refers to a family of statistical techniques that explores the relationships among a set of variables. Structural equation modeling provides an extremely versatile method to model very specific hypotheses involving systems of variables, both measured and unmeasured. Researchers can use SEM to study patterns of…
Descriptors: Gifted, Structural Equation Models, Factor Analysis, Enrichment
