Educational experiences of U.S. children during the 2020-2021 school year in the context of the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is a historic event impacting children around the globe. Prior research on the educational experiences of children during the COVID-19 pandemic focused almost exclusively on spring 2020. This article extends this literature past the initial shock of spring 2020, capturing the first full school year (2020-2021) during the COVID-19 pandemic. This registered report study utilized a national sample of 1,666 United States twins in kindergarten through 12th grade from 43 states to provide the current descriptive report of children's educational experiences during this time, as reported by their parents. Specifically, we reported on school format, parents' role in education, parent-teacher interactions, schoolwork struggles, technology access, and school services. About half of children attended in-person schooling, with many children switching from online to in-person throughout the school year. Parents saw the pandemic as a risk to their children's education. During the 2020-2021 school year of the pandemic, parents felt they had a larger role in their children's education and were less satisfied in their interactions with teachers than what they experienced during the prepandemic part of the 2019-2020 school year. Children experienced more schoolwork struggles than they had in previous years, and this was similar across age groups. For most constructs, results were equivalent across age group, but parents of younger children tended to provide more schoolwork help. Overall, this article highlights the disruptions in their educational environments that children continued to experience through the first full school year (2020-2021) of the COVID-19 pandemic. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Investigation of Two Preliminary Analysis-Altering Elements: Ordinate Scaling and DPPXYR.

The purpose of this pre-registered study (Peltier & McKenna) was to conceptually replicate if the truncation of the ordinate and DPPXYR increased analysts' estimation of a functional relation and magnitude of treatment effect. Visual analysts (n = 27) evaluated eight data sets reporting null (n = 2), small (n = 2), moderate (n = 2), and large (n = 2) effects. Each data set was graphed six times with manipulations of the ordinate and DPPXYR, resulting in 48 ABAB graphs. We estimated two separate three-level mixed effect models with variations nested in datasets and nested in participants to evaluate the impact of graph characteristics for (1) confidence in determining a functional relation and (2) the estimated magnitude of the treatment effect. We included ordinate scaling and DPPXYR at level 1 and graph effect size at level 2, including all interactions. Overall, graph manipulation consistently did not impact confidence in a functional relation. Results suggest mixed findings for graph manipulation on the estimated magnitude of the treatment effect. Findings will be couched in current literature and recommendations for graph construction and future research will be discussed.

Understanding Heritability in the Context of Reading Ability and Instruction.

In this paper, we discuss what heritability is and how it is measured, and explain why estimates of heritability are not always the same in different scientific papers. After providing this foundational knowledge, we bust some common myths about heritability. We end with discussing how teachers can use their knowledge about heritability in their own practice.

A How-To Guide for Open-Science Practices in Special Education Research.

Open-science reforms, which aim to increase credibility and access of research, have the potential to benefit the research base in special education, as well as practice and policy informed by that research base. Awareness of open science is increasing among special education researchers. However, relatively few researchers in the field have experience using multiple open-science practices, and few practical guidelines or resources have been tailored to special education researchers to support their exploration and adoption of open science. In this paper, we described and provided guidelines and resources for applying five core open-science practices-preregistration, registered reports, data sharing, materials sharing, and open-access publishing-in special education research.

Exploring Individual Differences in Response to Reading Intervention: Data from Project KIDS (Kids and Individual Differences in Schools).

This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442 students collected via parent survey in 2013. All data is currently stored on an online data repository and freely available. Data might be of interest to researchers interested in individual differences in reading development and response to instruction and intervention, as well as to instructors of data analytic methods such as hierarchical linear modeling and psychometrics.

Screening screeners: calculating classification indices using correlations and cut-points.

Given the recent push for universal screening, it is important to take into account how well a screener identifies children at risk for reading problems as well as how screener and sample information contribute to this classification. Picking the best cut-point for a particular sample and screening goal can be challenging given that test manuals often report classification information for a specific cut-point and sample base rate which may not generalize to other samples. By assuming a bivariate normal distribution, it is possible to calculate all of the classification information for a screener based on the correlation between the screener and outcome, the cut-point on the outcome (i.e., the base rate in the sample), and the cut-point on the screener. We provide an example with empirical data to validate these estimation procedures. This information is the basis for a free online tool that provides classification information for a given correlation between screener and outcome and cut-points on each. Results show that the correlation between screener and outcome needs to be greater than .9 (higher than observed in practice) to obtain good classification. These findings are important for researchers, administrators, and practitioners because current screeners do not meet these requirements. Since a correlation is dependent on the reliability of the measures involved, we need screeners with better reliability and/or multiple measures to increase reliability. Additionally, we demonstrate the impact of base rate on positive predictive power and discuss how gated screening can be useful in samples with low base rates.

Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?

Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.

Meeting the Moment: Impact of TEACH Grant on US Undergraduate Education Degree Completion in High-Need Content Areas.

As part of the College Cost Reduction and Access Act (2007), the USA funded the TEACH Grant to incentivize earning a degree in a high-need content area (e.g., STEM fields, language-related areas, and Special Education) and to help meet teacher supply needs in low-income schools. Our analysis investigates the impact TEACH has had on the production of undergraduate education degrees overall and in high-need content areas. Using publicly available datasets and propensity score methods, we compare undergraduate education degree production at institutions of higher education, making comparisons between adopters and non-adopters of TEACH. Our findings suggest the adoption of TEACH had no impact on the overall production of undergraduate education degrees or production of education degrees in STEM, language-related fields, or special education. We situate our findings in the context of unrelenting demand for teachers in the USA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1057/s41307-022-00263-3.

Open Science in Education Sciences.

The Open Science movement has gained considerable traction in the last decade. The Open Science movement tries to increase trust in research results and open the access to all elements of a research project to the public. Central to these goals, Open Science has promoted five critical tenets: Open Data, Open Analysis, Open Materials, Preregistration, and Open Access. All Open Science elements can be thought of as extensions to the traditional way of achieving openness in science, which has been scientific publication of research outcomes in journals or books. Open Science in education sciences, however, has the potential to be much more than a safeguard against questionable research. Open Science in education science provides opportunities to (a) increase the transparency and therefore replicability of research and (b) develop and answer research questions about individuals with learning disabilities and learning difficulties that were previously impossible to answer due to complexities in data analysis methods. We will provide overviews of the main tenets of Open Science (i.e., Open Data, Open Analysis, Open Materials, Preregistration, and Open Access), show how they are in line with grant funding agencies' expectations for rigorous research processes, and present resources on best practices for each of the tenets.