I recently completed my Ph.D. in the Educational Psychology program at the Graduate Center, CUNY, specializing in Learning, Development, and Instruction. In the process of completing my doctoral education, I also completed a certificate in Instructional Technology and Pedagogy. Prior to studying at the Graduate Center, I received a B.A. in Psychology from Cornell University and an M.A. in Cognitive Studies in Education from Teachers College, Columbia University. Since completing my doctorate, I am now a Postdoctoral Research Associate at the University of Notre Dame working in the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) Lab. I currently contribute to a project studying factors that influence high school students’ achievement in statistics courses.
"Examining Relations Between Executive Functions and Decoding: A Meta-analytic Investigation"
Previous meta-analyses highlight the role of executive functions (EF), encompassing working memory updating, task-switching, and inhibitory control, in reading comprehension, but have not established their role in decoding. Decoding is defined as the use of orthographic patterns to access oral pronunciations. According to the dual route model, decoding involves parallel activation of lexical and phonological routes, which places cognitive demands on EF. We used multivariate meta-analyses to examine associations between decoding, assessed via nonword and word reading tasks, and EF across studies involving children and adolescents. Meta-regression analyses examined a broad set of potential moderators of correlational effect size estimates, including variables related to sample characteristics, task features, and study design. Results indicated significant small-to-moderate correlations between EF constructs and decoding tasks (rz ranged from .20 to .37), with little evidence of moderation. The observed associations between EF and decoding skills in children and adolescents appear to be relatively consistent, even when accounting for moderators related to the sample, task, and study design.
Dr. Bruce Homer (chairperson); Dr. Patricia J. Brooks; Dr. David Rindskopf
Acknowledgements / Special Thanks:
Over the past several years, CUNY has been my home. The Graduate Center, Hunter College, and the University at-large, have provided me with the space and resources to purse scholarship, as well as the financial support to ease my practical concerns about the minutiae of physical existence. As a public institution, it has provided me so much, and I am beholden.
In the course of pursuing my doctoral dissertation and even prior, I have received support and encouragement from many individuals. Dr. Bruce Homer has been a great mentor, and now also a colleague and friend. I am grateful to other members of my dissertation committee, including Dr. Patricia Brooks, Dr. David Rindskopf, Dr. Linnea Ehri, and Dr. Klara Marton for their guidance as I pursued my dissertation project. In addition, I am thankful for the help of Dr. Jay Verkuilen, who provided statistical advice, as well as Dr. Roseanne Flores and Dr. Neal Rubin, who provided a pillar of encouragement throughout the process of writing.
I am grateful to abovementioned scholars, as well as all of my past instructors and mentors who have inspired me to become more curious, thoughtful, and diligent. I am grateful for my peers, as well. To Yilin Wang and Gail Swingler, who spent many hours coding manuscripts used in my analyses, I am immensely grateful. To my lab mates in both the ChILD and CREATE Labs, who raised questions and provided insights about phenomena in psychology and the learning sciences throughout my doctoral studies, I owe much gratitude and am eager to see your work to come. To all those who helped me as I tried to juggle teaching and service obligations, as well as dissertation work, I am dutifully indebted.
I would also like to thank my family and friends, including Chris and Pat, to whom I owe almost everything, and Thomas, Gwen, and Regina, who have also formatively contributed to my existence, as well as JKW, whose presence has been an unconditional source of joy and encouragement.
As a current postdoctoral researcher in the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) Lab at the University of Notre Dame, I study factors that influence high school students’ achievement in statistics courses. The project involves an adaptive assessment of statistics knowledge, and as such, I am learning about the design and validation of a computerized adaptive assessment. My current work in the lab concentrates on the accuracy of students’ predictions of their scores in relation to actual performance, and whether certain factors (e.g., course engagement, prior math experience, teacher support) mediate this association. My future work in the immediate future will concentrate on the associations between students’ subjective reports of difficulty and the relation to item-response theory calibrated indices of item difficulty from a computerized adaptive test of statistics knowledge.
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