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Early science, technology, engineering, and math (STEM) education matters for young children’s learning and their academic trajectories (McClure et al., 2017), but teachers must be empowered and su...

New Mexico is one of 44 U.S. states offering a public pre-K program for children at age 4. State models for pre-K vary in terms of availability, policies related to classroom quality, and populations of children served. In this study, we pool data from five successive cohorts of children (total N = 5218) using regression-discontinuity models to estimate the impacts of participating in New Mexico’s pre-K program on young children’s language, literacy, and math skills at kindergarten entry. Positive, statistically significant impacts of pre-K were found for each of these academic domains. Due to the high level of diversity in our sample, it was also possible to examine pre-K impacts separately for White, Hispanic, and Native American children. The largest impacts were found for White and Hispanic children, with less consistent and more modest impacts for Native American children. These findings suggest that while New Mexico’s pre-K program generated academic benefits for children, not all groups of children benefited equally, and further information is needed to understand the reasons for these differences.

Relatively few studies provide rigorous estimates of the long-term effects of large-scale public preschool programs, and their findings vary greatly. This study investigates the effects in third through tenth grade of New Jersey's Abbott preschool program which has many of the features and contexts hypothesized to mitigate fadeout. The program was designed and implemented in the context of a Court mandated systemic reform of education and its funding from preschool through high school. We describe in detail the program's features including an extensive, multitiered continuous improvement system. Sample size for analyses ranged from 426 to 785 depending on the grade and assessment. Participants were primarily Black and Hispanic students living in 31 communities with high concentrations of poverty. Inverse weighting by propensity scores was employed with multiple imputation for missing data to estimate effects on achievement, grade retention, and special education. Substantial positive effects were found in language arts and literacy, mathematics, and science on statewide assessments. Effects did not fade after grade 3. Achievement effects appear to be larger for 2-year than 1-year of the preschool program. Grade retention was significantly lower through grade 10. Effects on special education placement were imprecisely estimated but consistent with other findings of reduced special education. Results were robust with respect to alternative methods to control for measured and unmeasured differences between preschool and comparison groups and for missing data. This study adds to the evidence on preschool program features and contexts associated with long-term effects.

Traditional in-app virtual reality (VR)/augmented reality (AR) applications pose a challenge of reaching users due to their dependency on operating systems (Android, iOS). Besides, it is difficult for general users to create their own VR/AR applications and foster their creative ideas without advanced programming skills. This paper addresses these issues by proposing an interactive extended reality toolkit, named BlocklyXR. The objective of this research is to provide general users with a visual programming environment to build an extended reality application for digital storytelling. The contextual design was generated from real-world map data retrieved from Mapbox GL. ThreeJS was used for setting up, rendering 3D environments, and controlling animations. A block-based programming approach was adapted to let users design their own story. The capability of BlocklyXR was illustrated with a use case where users were able to replicate the existing PalmitoAR utilizing the block-based authoring toolkit with fewer efforts in programming. The technology acceptance model was used to evaluate the adoption and use of the interactive extended reality toolkit. The findings showed that visual design and task technology fit had significantly positive effects on user motivation factors (perceived ease of use and perceived usefulness). In turn, perceived usefulness had statistically significant and positive effects on intention to use, while there was no significant impact of perceived ease of use on intention to use. Study implications and future research directions are discussed.

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. This work-in-progress paper focuses on misinformation in graphical representations utilizing a case-based learning approach. The misleading data visualization examples are surveyed and projected onto fundamental units of visual communication, such as size, value, shape, size, and position. This work aims at helping viewers understand the root causes of the misuse, as well as provide basic principles for making more effective visualizations.

Social media platforms are widely used nowadays by various businesses to promote their products and services through multimedia content. Instagram is one of those platforms, which is used not only by companies to promote their products but also by local governments to promote tourist destinations. Predicting the popularity of the promotional tourist destination images helps marketers to plan strategically. However, given the abundance of images posted on Instagram daily, identifying the factors that determine the popularity of an image is a big challenge, due to informal and noisy visual content, frequent content evolution, a lack of explicit visual elements, and people&rsquo, s informal behavior in liking, commenting on, and viewing the images. We present an approach to identify the factors most responsible for the popularity of tourist destinations-related images on Instagram. Our approach provides a proof of concept for an artificial intelligence (AI)-based real-time content management system, which will help to promote a tourist destination. The experiments on a collection of posts crawled from the official Instagram account of Jeju Island, which is one of the most popular tourist destinations in Korea, show that the recency of the post is the most important predictor of the number of likes and comments it will receive. Moreover, the combination of visual content and context features is an excellent predictor of popularity. The number of likes and comments are found to be complementary to each other for predicting image popularity.

A fundamental assumption underlying latent class analysis (LCA) is that class indicators are conditionally independent of each other, given latent class membership. Bayesian LCA enables researchers to detect and accommodate violations of this assumption by estimating any number of correlations among indicators with proper prior distributions. However, little is known about how the choice of prior may affect the performance of Bayesian LCA. This article presents a Monte Carlo simulation study that investigates (1) the utility of priors in a range of prior variances (i.e., strongly non-informative to strongly informative priors) in terms of Type I error and power for detecting conditional dependence and (2) the influence of imposing approximate independence on model fit of Bayesian LCA. Simulation results favored the use of a weakly informative prior with large variance–model fit (posterior predictive p–value) was always satisfactory when the class indicators were either independent or dependent. Based on the current findings and the additional literature, this article offers methodological guidelines and suggestions for applied researchers.

Answer Set Programming (ASP) is a dominant programming paradigm in Knowledge Representation. It is used to build intelligent agents – knowledge-intensive software systems capable of exhibiting intelligent behaviors. It is found that ASP can also be used to teach computer science in middle and high schools. However, the current ASP systems do not provide direct support for a programmer to produce an intelligent agent that a general user can directly interact with, which may greatly compromise the potential attraction of ASP to the secondary school students. In this paper, we propose a Virtual Reality (VR) programming environment called VRASP that allows a student to produce an avatar (agent) in a virtual world that is able to answer questions in spoken natural language from a general user. The VR application is accessible from anywhere so that the students’ friends can interact with the agent. As a result, it gives the students a feeling of achievement and thus encourages them to solve problems using ASP. VRASP was evaluated with 10 users. Results of these studies show that students are able to communicate with the environment intuitively with an accuracy of 78%.

Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

Previous research has established that the left cerebral hemisphere is dominant for the control of continuous bimanual movements. The lateralisation of motor control for discrete bimanual movements, in contrast, is underexplored. The purpose of the current study was to investigate which (if either) hemisphere is dominant for discrete bimanual movements. Twenty-one participants made bimanual reach-to-grasp movements towards pieces of candy. Participants grasped the candy to either place it in their mouths (grasp-to-eat) or in a receptacle near their mouths (grasp-to-place). Research has shown smaller maximum grip apertures (MGAs) for unimanual grasp-to-eat movements than unimanual grasp-to-place movements when controlled by the left hemisphere. In Experiment 1, participants made bimanual symmetric movements where both hands made grasp-to-eat or grasp-to-place movements. We hypothesised that a left hemisphere dominance for bimanual movements would cause smaller MGAs in both hands during bimanual grasp-to-eat movements compared to those in bimanual grasp-to-place movements. The results revealed that MGAs were indeed smaller for bimanual grasp-to-eat movements than grasp-to-place movements. This supports that the left hemisphere may be dominant for the control of bimanual symmetric movements, which agrees with studies on continuous bimanual movements. In Experiment 2, participants made bimanual asymmetric movements where one hand made a grasp-to-eat movement while the other hand made a grasp-to-place movement. The results failed to support the potential predictions of left hemisphere dominance, right hemisphere dominance, or contralateral control.

A simulation based comparative study was designed to compare two alternative approaches to structural equation modeling—generalized structured component analysis (GSCA) with the alternating least squares (ALS) estimator vs. covariance structure analysis (CSA) with the maximum likelihood (ML) estimator or the weighted least squares mean and variance adjusted (WLSMV) estimator—in terms of parameter recovery with ordinal observed variables. The simulated conditions included the number of response categories in observed variables, distribution of ordinal observed variables, sample size, and model misspecification. The simulation outcomes focused on average root mean square error (RMSE) and average relative bias (RB) in parameter estimates. The results indicated that, by and large, GSCA-ALS recovered structural path coefficients more accurately than CSA-ML and CSA-WLSMV in either a correctly or incorrectly specified model, regardless of the number of response categories, observed variable distribution, and sample size. In terms of loadings, CSA-WLSMV outperformed GSCA-ALS and CSA-ML in almost all conditions. Implications and limitations of the current findings are discussed, as well as suggestions for future research.

Caregiver responsiveness has been theorized and found to support children’s early executive function (EF) development. This study examined the effects of an intervention that targeted family child care provider responsiveness on children’s EF. Family child care providers were randomly assigned to one of two intervention groups or a control group. An intervention group that received a responsiveness-focused online professional development course and another intervention group that received this online course plus weekly mentoring were collapsed into one group because they did not differ on any of the outcome variables. Children ( N = 141) ranged in age from 2.5 to 5 years (mean age = 3.58 years; 52% female). At pretest and posttest, children completed delay inhibition tasks (gift delay-wrap, gift delay-bow) and conflict EF tasks (bear/dragon, dimensional change card sort), and parents reported on the children’s level of attention problems. Although there were no main effects of the intervention on children’s EF, there were significant interactions between intervention status and child age for delay inhibition and attention problems. The youngest children improved in delay inhibition and attention problems if they were in the intervention rather than the control group, whereas older children did not. These results suggest that improving family child care provider responsive behaviors may facilitate the development of certain EF skills in young preschool-age children.

This paper explores a way to apply Item Response Theory (IRT), one of the popular statistical methodologies in measurement and psychometrics, to evaluate Financial Transmission Rights (FTR) paths in the U.S. electricity market. FTR is an energy derivative product to hedge congestion cost risks inherent in constrained transmission lines. In New England, with about 1200 pricing locations, the theoretical combinations of FTR paths amount to 1.4 million in prevailing flows alone. With capital constraints, it is imperative that FTR market participants build the capability to evaluate FTR paths to bid on. IRT provides a framework of how well tests work, and how individual items work on tests, estimating respondents&rsquo, latent abilities, and individual item parameters. IRT is utilized to analyze historical electricity data of 2019 for a daily congestion cost of eight customer load zones and one hub in the U.S., New England, for the evaluation of FTR paths. In the analysis, an item represents an FTR path, while item difficulty, item discrimination, and a latent trait variable for the path correspond to the path profitability, risk level, and daily congestion ability, respectively. This paper explores the experimental procedures by which IRT, a psychometric tool, may also be applicable in complex energy markets, providing a consistent and standardized analytical framework to address the issues of selection and prioritization among multiple opportunities. FTR path evaluation is conducted in three steps to determine bid priority paths in FTR auctions: parameter significance tests, ranking on path profitability and risk level, and weighting scores of individual rankings on the two criteria.

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCAM, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite of indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCAM and existing methods. These methods are also applied to fit a substantively well-established model to real data.

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

State-funded prekindergarten (preK) programs are increasingly common across the country. This study estimated the effects of eight state-funded preK programs (Arkansas, California, Michigan, New Jersey, New Mexico, Oklahoma, South Carolina, and West Virginia) on children’s learning using a regression discontinuity design. These programs vary with respect to the population served, program design, and context. Weighted average effect sizes from instrumental variables analyses across these states are 0.24 for language (vocabulary), 0.44 for math, and 1.10 for emergent literacy. Differences in effect sizes by domain suggest that preK programs should attend more to enhancing learning beyond simple literacy skills. State preK programs appear to differ in their effects. We offer recommendations for more rigorous, regular evaluation.