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Steenbergen-Hu, Saiying; Olszewski-Kubilius, Paula – Journal for the Education of the Gifted, 2017
In this study, we conducted binary logistic regression on survey data collected from 244 past participants of a Talent Search program who attended regular high schools but supplemented their regular high school education with enriched or accelerated math and science learning activities. The participants completed an online survey 4 to 6 years…
Descriptors: Academic Achievement, STEM Education, Educational Experience, Interests
Soria, Krista M.; Stubblefield, Robin – Journal of The First-Year Experience & Students in Transition, 2014
As strengths-based approaches continue to gain steady momentum in colleges and universities, a distinct need for scholarship on the benefits of strengths-based practices has emerged. In fall 2011, all first-year students at a university in the Midwest were invited to discover their strengths by taking the Clifton StrengthsFinder. The purpose of…
Descriptors: College Freshmen, Learner Engagement, Academic Achievement, Self Efficacy
Soria, Krista M.; Stubblefield, Robin – Journal of College Student Retention: Research, Theory & Practice, 2015
This goal of this study was to examine the benefits of a campus-wide initiative to build first-year students' strengths awareness. The results of a survey of the first-year class (n?=?1,421) suggest a positive and significant relationship between students' strengths awareness and their belief that the strengths initiatives on campus positively…
Descriptors: College Freshmen, Student Attitudes, Group Membership, Interpersonal Relationship
Baker, Joseph; Cote, Jean – High Ability Studies, 2003
Several sport-specific talent detection models have been developed over the last 30 years (Durand-Bush & Salmela, 2001). However, these models have failed in at least one important standard of judgment--accurately predicting who will develop into an elite level athlete. The authors believe that the WICS model presented by Robert Sternberg also…
Descriptors: High Achievement, Talent Identification, Models, Predictor Variables

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