Dr. Saiying Steenbergen-Hu
Research Director, Center for Talent Development
Research Assistant Professor, Education and Social Policy
BiographySaiying Steenbergen-Hu, Ph.D., is a research assistant professor and the research director of the Center for Talent Development (CTD) of Northwestern University’s School of Education and Social Policy. Her professional experience includes working as postdoctoral fellow in the Department of Psychology & Neuroscience at Duke University from 2009 to 2013, a lecturer and researcher at the University of Science and Technology of China (USTC). She holds a Ph.D. in educational psychology from Purdue University.
Her research focuses on the effectiveness of educational interventions on students’ academic achievement and psychosocial development. She conducts research on topics relevant to gifted education and talent development, including the long-term effects of participation in acceleration and enrichment programs, executive functioning of gifted students, and evidence-based interventions for high-potential learners. Steenbergen-Hu is an author of more than a dozen refereed journal articles and book chapters.
Her methodological interests and skills include meta-analysis, research synthesis, educational measurement and assessment, quantitative research methodology, and applied statistical analysis. Her meta-analysis, coauthored with Sidney Moon, on the effects of acceleration on high-ability learners won the Gifted Child Quarterly (GCQ) Paper of the Year Award in 2012. Steenbergen-Hu, Makel, and Olszewski-Kubilius recently published “What one hundred years of research says about ability-grouping and acceleration: Findings of two second-order meta-analysis” in Review of Educational Research (Volume 86, Issue no.4, pp.849-899, 2016).View Microsoft Word DOC file of Saiying Steenbergen-Hu's biography.
Curriculum VitaeView Saiying Steenbergen-Hu's CV.
- 2012 - Paper of the Year on Gifted Child Quarterly
|2009||PhD, Educational Psychology||Purdue University|
|2000||MA||University of Science & Technology of China|
|2009||The effects of acceleration on high-ability learners: A meta-analysis||Download Adobe Acrobat PDF|
|2015||Online curriculum consortium for accelerating middle school (Project OCCAMS)||Department of Education, Javits Gifted and Talented Students Education Program Grants||2015 - 2018||$1.2 millions||Funded|
PI: Calvert, Eric Steenbergen-Hu, Saiying Olszewski-Kubilius, Paula
|2015||School-based executive functioning interventions for improving executive functions, academic, social-emotional, and behavioral outcomes in school-age children and adolescents: A systematic review and||The Jacobs Foundation and the Campbell Collaboration||2015 - 2017||$50,000||Funded|
PI: Steenbergen-Hu, Saiying Olszewski-Kubilius, Paula Calvert, Eric
|2014||Do Academically Gifted Children and Adolescents Also Score Well in Executive Functions?||The Esther Katz Rosen Fund Grants, American Psychological Foundation||2014 - 2016||$43,500||Funded|
PI: Steenbergen-Hu, Saiying Calvert, Eric
Research InterestsAcademic achievement, psychological and social-emotional development, educational measurement and assessment, meta-analysis and research synthesis, quantitative research methods, executive functions, gifted education and talent development
Factors that contributed to gifted students’ su |
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 after high school. This study examined how their demographics, high school experiences, and timing of and reasons for pursuing a science, technology, engineering, and mathematics (STEM) pathway related to the probability of earning STEM college degrees. This study revealed two factors that were positively and significantly associated with the outcome of earning STEM college degrees: Asian or White ethnicity and students’ personal interest in STEM. Findings suggest that students’ success in earning STEM degrees may not be fully attributable to their high achievements or abilities, and that their experiences in the Talent Search and supplemental outside-of-school gifted programs helped students intensify their interests in STEM.
What One Hundred Years of Research Says About the |
Two second-order meta-analyses synthesized approximately 100 years of research on the effects of ability grouping and acceleration on K–12 students’ academic achievement. Outcomes of 13 ability grouping meta-analyses showed that students benefited from within-class grouping (0.19 ≤ g ≤ 0.30), cross-grade subject grouping (g = 0.26), and special grouping for the gifted (g = 0.37), but did not benefit from between-class grouping (0.04 ≤ g ≤0.06); the effects did not vary for high-, medium-, and low-ability students. Three acceleration meta-analyses showed that accelerated students significantly outperformed their nonaccelerated same-age peers (g = 0.70) but did not differ significantly from nonaccelerated older peers (g = 0.09). Three other meta-analyses that aggregated outcomes across specific forms of acceleration found that acceleration appeared to have a positive, moderate, and statistically significant impact on students’ academic achievement (g = 0.42).
How to conduct a good meta-analysis in gifted educ |
This methodological brief introduces basic procedures and issues for conducting a high-quality meta-analysis in gifted education. Specifically, we discuss issues such as how to select a topic and formulate research problems, search for and identify qualified studies, code studies and extract data, choose and calculate effect sizes, analyze data, conduct heterogeneity and moderator analysis, assess publication bias, as well as interpret and report meta-analysis results. We discuss why meta-analysis is needed in gifted education. We also review the history of meta-analyses in gifted education and discuss topics that are conducive to meta-analysis. Furthermore, we provide an overview of the challenges and recent advancements in meta-analysis methodology. Last, we introduce useful resources for further learning of meta-analysis.
Last Updated: 2017-09-13 17:16:54