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REL Central Ask A REL Response

Early Childhood

December 2019

Question

What are the indicators for academic risk and/or school failure for children younger than 5 years old?

Response

Following an established research protocol, REL Central conducted a search for research reports as well as descriptive study articles to help answer the question. The resources included ERIC and other federally funded databases and organizations, research institutions, academic databases, and general Internet search engines. (For details, please see the methods section at the end of this memo.)

References are listed in alphabetical order, not necessarily in order of relevance. We have not evaluated the quality of the references provided in this response, and we offer them only for your information. We compiled the references from the most commonly used resources of research, but they are not comprehensive and other relevant sources may exist.

Research References

Blazer, C. (2012). Pre-kindergarten: Research-based recommendations for developing standards and factors contributing to school readiness gaps (Information Capsule, Vol. 1201). Miami-Dade County Public Schools, Office of Assessment, Research, and Data Analysis, Research Services https://eric.ed.gov/?id=ED536524

From the abstract:

“States across the country are developing pre-kindergarten standards that articulate expectations for preschooler’s learning and development and define the manner in which services will be provided. There are two different types of standards: student outcome standards and program standards. Student outcome standards define the knowledge and skills children are expected to demonstrate by the end of their preschool year. Program standards describe characteristics of the preschool program, such as required teacher qualifications and student-teacher ratio. This Information Capsule provides a summary of research-based recommendations for policymakers and educators who are developing each type of standard.

This paper also reviews factors that contribute to gaps in children’s preschool readiness. The factor that has been found to correlate most highly with preschool learning disparities is family income level. Children’s home learning environment, parents’ level of educational attainment, ethnic and cultural influences, as well as parental beliefs and behaviors are also related to school readiness and school performance outcomes. However, since most of these factors are strongly tied to socioeconomic status, researchers have concluded that income level is the most powerful predictor of children’s educational success. A brief discussion of the research, indicating that participation in high-quality preschool programs can significantly reduce early learning disparities by diminishing the negative effects of family and environmental risk factors, is included. Finally, a description of Miami-Dade County Public Schools’ pre-K programs is provided.”


Cadima, J., McWilliam, R. A., & Leal, T. (2010). Environmental risk factors and children’s literacy skills during the transition to elementary school. International Journal of Behavioral Development, 34(1), 24–33. Retrieved from https://eric.ed.gov/?id=EJ867055.
Full text available at https://www.researchgate.net/profile/Joana_Cadima/publication/234759615_Environmental_Risk_Factors_and_Children%27s_Literacy_Skills_during_the_Transition_to_Elementary_School/links/00b4951f2eaef6cbec000000.pdf

From the abstract:

“This study examined the effects of the accumulation of family risk factors on children’s literacy skills, both in preschool and in first grade. Children’s (N = 106) vocabulary, conventions of print, phonological awareness, knowledge of letters, reading decoding, and reading comprehension were assessed. Family risk factors, consisting of household composition, years of maternal education, job situation of the mother, and income level of the family, were combined to create a cumulative risk index. Canonical correlation and multiple regression analyses were performed. Results revealed the negative impact of cumulative risk index on both the preschool and first-grade literacy skills. In addition, the number of risk factors present in the family context negatively predicted the majority of the first-grade literacy skills, after taking preschool skills into account. The results provide further evidence of the negative impact of the accumulation of family risks on child literacy development and call attention to the importance of early experiences for later academic achievement.”


Joy, J. M. (2016). Evaluating positive social competence in preschool populations. School Community Journal, 26(2), 263–289. Retrieved from https://eric.ed.gov/?id=EJ1124016

From the abstract:

“Social competence is seen as a critical aspect of academic and social success; however, the construct is often minimized to a set of social skills or the absence of negative behaviors. The current study aims to broaden the understanding of social competence by incorporating the factors associated with the development of social competence and the outcomes associated with social competence into one model. Additionally, the multifaceted construct of positive social competence included in the model is entirely positively framed. Participants in the current study were 153 sets of parents and children attending preschool in a large suburban preschool program in Colorado. Structural equation modeling was used to simultaneously examine how early risks and protective factors relate to social competence and how social competence relates to outcomes (social school readiness and self-concept/self-esteem). Data resulted in a well fitting model overall. Significant pathways were found between Child’s Self-Regulation and Positive Social Competence and between Positive Social Competence and the two other endogenous variables in the model (i.e., variables explained by other variables in the model), namely Social School Readiness and Self-Concept/Self-Esteem.”


Kluczniok, K. (2017). Early family risk factors and home learning environment as predictors of children’s early numeracy skills through preschool. SAGE Open, 7(2), 1–13. Retrieved from https://eric.ed.gov/?id=EJ1197930.
Full text available at https://doi.org/10.1177%2F2158244017702197

From the abstract:

“The present study examines the impact of family risk factors (e.g., migration background, poverty) in early childhood on children’s numeracy skills during preschool in Germany, and if these relations are mediated through the quality of the home learning environment. The data used for this research were collected using the longitudinal study BiKS-3-10 which followed 547 children from the first (average age: 3 years) to the third year (average age: 5 years) of preschool. The hypothesized mediation of quality of the home learning environment can only be interpreted using the home learning environment scale for cognitive promotion. In contrast, the quality of the home learning environment, specifically family support factors, is related neither to children’s development in numeracy nor to family risk. The results highlight the impact of early risk factors on children’s competencies and the mediating role of the quality of the home learning environment.”


Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Who is at risk for persistent mathematics difficulties in the United States? Journal of Learning Disabilities, 49(3), 305–319. Retrieved from https://eric.ed.gov/?id=EJ1096202.
Full text available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4422774/

From the ERIC abstract:

“We analyzed two nationally representative, longitudinal data sets of U.S. children to identify risk factors for persistent mathematics difficulties (PMD). Results indicated that children from low socioeconomic households are at elevated risk of PMD at 48 and 60 months of age, as are children with cognitive delays, identified developmental delays or disabilities, and vocabulary difficulties. In contrast, children attending preschool either in Head Start or non-Head Start classrooms are at initially lower risk of PMD. Kindergarten-aged children experiencing either low socioeconomic status or mathematics difficulties are at greatest risk for PMD across third, fifth, and eighth grades. Also at risk for PMD between third and eighth grades are children displaying reading difficulties or inattention and other learning-related behaviors problems, children with identified disabilities, and those who are retained. Educationally relevant and potentially malleable factors for decreasing young children’s risk for PMD may include increasing children’s access to preschool, decreasing their risk of experiencing vocabulary or reading difficulties, and avoiding use of grade retention.”


Thompson, P. A., Hulme, C., Nash, H. M., Gooch, D., Hayiou–Thomas, E., & Snowling, M. J. (2015). Developmental dyslexia: Predicting individual risk. Journal of Child Psychology and Psychiatry, 56(9), 976–987. Retrieved from https://eric.ed.gov/?id=EJ1071290.
Full text available at https://doi.org/10.1111/jcpp.12412

From the ERIC abstract:

Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known.
Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as ‘dyslexic’ or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes.
Results: Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level.
Conclusions: Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery.”


Zambrana, I. M., Pons, F., Eadie, P., & Ystrom, E. (2014). Trajectories of language delay from age 3 to 5: Persistence, recovery and late onset. International Journal of Language & Communication Disorders, 49(3), 304–316. Retrieved from https://eric.ed.gov/?id=EJ1024824.
Full text available at https://doi.org/10.1111/1460-6984.12073

From the ERIC abstract:

Background: Knowledge is scarce on what contributes to whether children with early language delay (LD) show persistent, recovering or sometimes late-onset LD without a prior history of early LD in subsequent preschool years.
Aims: To explore whether an integrative model of vital risk factors, including poor early communication skills, family history of language-related difficulties and male gender, predicts the development of persistent, recovering or late-onset LD trajectories from 3 to 5 years quantitatively and qualitatively differently.
Methods & Procedures: LD was assessed by maternal reports on the Ages and Stages Questionnaire at 3 and 5 years for 10,587 children in The Norwegian Mother and Child Cohort Study. Children were classified across time as having no, late onset, transient or persistent LD. Multinomial logistic regression analyses included the integrative model of vital risk factors and covariates.
Outcome & Results: Across time, 3%, 5% and 6.5% of the children displayed persistent, transient and late-onset LD, respectively. The odds for persistent LD were doubled for boys and children with low language comprehension at 1.5 years; and tripled by late-talking familial risk. These same odds decreased for transient LD, and even further for late-onset LD. Familial risk for writing and reading difficulties especially increased the odds for late-onset and persistent LD, while familial risk of unintelligible speech increased the odds for transient LD. Although girls had on average far better language comprehension than boys, low language comprehension was a stronger risk factor for persistent LD in girls.
Conclusions & Implications: Preschool LD trajectories were uniquely predicted from the integrative risk model of poor early communicative skills, family history and male gender. This might benefit identification of different LD trajectories by supporting broader severe vulnerability for persistent LD, milder vulnerability for transient LD, and possibly a specific risk for reading and learning difficulties for children with late-onset LD.”




Methods

Search Strings

The following keywords and search strings were used to search the reference databases and other sources:

  • Academic risk factors preschool
  • Failure AND preschool
  • “Risk factors” AND preschool
  • Risk factors in preschool

Databases and Resources

We searched ERIC for relevant resources. ERIC is a free online library of over 1.6 million citations of education research sponsored by the Institute of Education Sciences. Additionally, we searched Google Scholar.

Reference Search and Selection Criteria

When searching and reviewing resources, we considered the following criteria:

  • Date of the Publication: References and resources published between 2009 and 2019 were included in the search and review.
  • Search Priorities of Reference Sources: Search priority was given to ERIC, followed by Google Scholar.
  • Methodology: The following methodological priorities/considerations were used in the review and selection of the references: (a) study types–randomized control trials, quasi experiments, surveys, descriptive analyses, literature reviews; and (b) target population and sample.

This memorandum is one in a series of quick-turnaround responses to specific questions posed by educational stakeholders in the Central Region (Colorado, Kansas, Missouri, Nebraska, North Dakota, South Dakota, Wyoming), which is served by the Regional Educational Laboratory Central at Marzano Research. This memorandum was prepared by REL Central under a contract with the U.S. Department of Education’s Institute of Education Sciences (IES), Contract ED-IES-17-C-0005, administered by Marzano Research. Its content does not necessarily reflect the views or policies of IES or the U.S. Department of Education nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.