Skip site navigation
University of Maryland Division of Research
Who We Are Capabilities Partnerships Resources News
Analytical Nuclear Magnetic Resonance (NMR) Service & Research Center Biomolecular Nuclear Magnetic Resonance (NMR) Facility Biosciences Cores: Genomics, Imaging, and Flow Cytometry BioWorkshop Brain & Behavior Institute - Advanced Genomic Technologies Core CALCE Test Services and Failure Analysis Laboratory Center For Innovative Biomedical Resources (CIBR) Clarice Smith Performing Arts Center Daikin Energy Innovation Lab DLAR Imaging Core Exposome Small Molecule Core Facility Glenn L. Martin Wind Tunnel Herschel S. Horowitz Center for Health Literacy KIT-Maryland MEG Lab Maryland Fire and Rescue Institute (MFRI) Maryland NanoCenter Maryland Neuroimaging Center Mass Spectrometry Facility Michelle Smith Collaboratory for Visual Culture Neutral Buoyancy Research Facility (NBRF) Surface Analysis Center The Laboratory for Biological Ultrastructure The University of Maryland Center for Health Equity The University of Maryland Prevention Research Center X-ray Crystallographic Center (XCC)
Africa Through Language and Area Studies (ATLAS) Anti-Black Racism Initiative Effective and Equitable Weather Forecasting in a Changing Climate with Machine Learning Encuentros: A University-Community Partnership to Mitigate the Mental Health Crisis for Latino Immigrant Youth Fostering Inclusivity through Technology (FIT) Helping Our Bodies Clear Respiratory Infections The Maryland Safe Drinking WATER Study Modeling the Evolution of Avian Influenza Viruses Music Education for All Through Personalized AI and Digital Humanities Observing Wildfires Through UAVs and Fire Imaging Technologies Programmable Design of Sustainable, All-Natural Plastic Substitutes Racial and Social Justice Research-Practice Partnership Collaborative Remediation of Methane, Water, and Heat Waste Seizing Opportunities: Social Capital, Businesses, and Communities Using Machine Learning to Measure and Improve Equity in K-12 Mathematics Classrooms Water Emergency Team
Accurate, Equitable, and Transparent Genetic Ancestry Inference Advancing Environmental Justice By Evaluating Climate-Ready Urban Street Trees In Historically Redlined Neighborhoods AFTER: A Hospital Violence Intervention Program For Youth Victims of Gunshot Injury An Innovative Intervention to Help Asian American Families Cope with Racism and Mental Health Difficulties Bridging the Gaps in Satellite Observations of Earth Systems to Support Climate Monitoring and Prediction Climate Change and Political Conflict Climate Mitigation and Land-Use Digital Equity Mapping Research and Training Program Establishing a Role for Psilocybin in Frontal Lobe Function Fetal Mammary Stem Cell Programming and Hormone Dysfunction Forecasting Acute Malnutrition for Anticipatory Action Genetic and Lifestyle Risk Factors of Accelerated Brain Aging in Severe Mental Illness How Does Statistical Learning Interact with Socioeconomic Status to Shape Literacy Development? Human Rights Politics and Policies: Lessons from Latin America Increasing Sustainability, Accessibility, and Equity in Urban Mobility with A Self-driving E-Scooter Increasing Participation of Minorities and Women In STEM Through Sports Performance Analytics Research Market Design, Energy Storage, and Interconnection to the U.S. Power Grid On-board Energy Harvesting for Long-endurance Earth Observation UAVs Promoting Youth Mental Wellbeing in Rural Honduras by Engaging Teachers as Catalysts Relating Attitudes on Democracy to Attitudes on Race and Ethnicity An Innovative Approach to Remove Emerging Organic Contaminants from the Environment Role of Mitochondria Dynamics in Opioid Addiction Towards an Early Warning System for Increased Probability of Community Infection by SARS-Cov-2 Variants Understanding the Impact of Wind on Fire Dynamics in Mass-Timber Compartment Visualizing Urban Flooding Due To Climate Change
Search
Who We Are Capabilities Partnerships Resources News

Research Study Examines How Statistical Learning and Socioeconomic Status Shape Literacy Development

October 02, 2024

A project led by Prof. Min Wang in the Department of Human Development and Quantitative Methodology within the University of Maryland's College of Education is examining the relationship between socioeconomic status (SES), statistical learning (an intrinsic cognitive skill to detect probabilistic cues from environment), and reading development using a rigorous, two-wave longitudinal design among native Chinese-speaking children spanning ages 8–9 years. Research findings from the project, which is funded by the Grand Challenges Grants program, have educational implications for developing targeted statistical learning interventions to reduce SES disparities in literacy development in poor areas and regions across the world.

Dr. Wang has recently submitted a manuscript for review and publication entitled 'Statistical learning as a buffer: Investigating its impact on the link between home environment and reading achievement.'  In this study, Dr. Wang’s team recruited a sample of 191 eight-year-old Chinese children from diverse SES backgrounds who completed assessments of vocabulary knowledge, language related skills, nonlinguistic visual and auditory statistical learning, Chinese written language related statistical learning, reading outcomes, nonverbal reasoning, and verbal working memory. The key finding from the study is that children who are better in written language statistical learning were less affected by SES disparities. Written language related statistical learning may mitigate the effects of lower SES on reading development. The study provides initial evidence regarding the potential role of language-specific statistical learning as a buffer against the impact of SES disparity on reading achievement.

Dr. Wang also has published two other studies related to this project: 

Ren, J., Wang, M., Conway, C. M. (2024). Can explicit instruction boost statistical learning? A meta-analytical review. Journal of Educational Psychology,116(7), 1215–1237. https://doi.org/10.1037/edu0000897

Ren, J., & Wang, M. (2024). Contribution of statistical learning in reading across languages. PLoS ONE, 19(3), e0298670. https://doi.org/10.1371/journal.pone.0298670

In addition to these publications, Dr. Wang’s doctoral student Jinglei Ren, a key contributor to this Grand Challenges project, has recently received an offer for a post-doctoral position at the Haskins Laboratory at Yale University, starting January 2025, demonstrating a successful case of student training funded by this grant. 

For more information, visit: https://research.umd.edu/ses-reading