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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.

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.

Generalized structured component analysis (GSCA) has been extensively enhanced in terms of data-analytic capability and flexibility as well as computational efficiency. This article illustrates a novel application of GSCA for brain connectivity research, the purpose of which is to facilitate its uses with functional neuroimaging data among applied researchers and practitioners. Using data collected during encoding of source memory in a functional magnetic resonance imaging study, this article demonstrates how to specify and evaluate a fully and bidirectionally connected structural model of brain connectivity using GSCA. Implications of the GSCA approach and future directions for brain research are discussed.

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.

In this article, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement errors incorporated (GSCAM) in a unified manner and can estimate both factor- and component-model parameters, including component and factor loadings, component and factor path coefficients, and path coefficients connecting factors and components. We conduct 2 simulation studies to investigate the performance of IGSCA under models with both factors and components. The first simulation study assesses how existing approaches for structural equation modeling and IGSCA recover parameters. This study shows that only consistent partial least squares (PLSc) and IGSCA yield unbiased estimates of all parameters, whereas the other approaches always provided biased estimates of several parameters. As such, we conduct a second, extensive simulation study to evaluate the relative performance of the 2 competitors (PLSc and IGSCA), considering a variety of experimental factors (model specification, sample size, the number of indicators per factor/component, and exogenous factor/component correlation). IGSCA exhibits better performance than PLSc under most conditions. We also present a real data application of IGSCA to the study of genes and their influence on depression. Finally, we discuss the implications and limitations of this approach, and recommendations for future research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

State-of-the-art tools for creating augmented reality (AR) applications often depend on a specific programming language and the deployed target devices. The typing syntax of a program is error-prone, and device dependency makes it difficult to share newly created AR applications. This paper presents BlocklyAR, a novel web-based visual programming interface for creating and generating an AR application. This tool is intended for non-programmers (young learners and enthusiasts) who are interested in making an AR application. The goals of this tool are: (1) to help young learners and enthusiasts express their programming ideas without memorizing syntax, (2) to enable users to perceive their expressions, (3) to enable learners to generate an AR application with minimal effort, and (4) to support users by allowing them to share newly created AR applications with others. BlocklyAR uses Blockly for creating a palette of commands and AR.js for transcribing commands into AR experience. The applicability of BlocklyAR was demonstrated through a use case where an existing AR application was recreated by using our tool. The result showed that our tool could yield an equivalent product. We evaluated the visual tool with the help of 66 users to gather perspectives on the specific benefits of employing BlocklyAR in producing an AR application. The technology acceptance model was adapted to assess an individual&rsquo, s acceptance of information technology.

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%.

In recent years, with the development of modern technology, Virtual Reality (VR) has been proven as an effective means for entertaining and encouraging learning processes. Users immerse themselves in a 3D environment to experience situations that are very difficult or impossible to encounter in real life, such as volcanoes, ancient buildings, or events on a battlefield. Augmented Reality (AR), on the other hand, takes a different approach by allowing users to remain in their physical world while virtual objects are overlaid on physical ones. In education and tourism, VR and AR are becoming platforms for student learning and tourist attractions. Although several studies have been conducted to promote cultural preservation, they are mostly focused on VR for historical building visualization. The use of AR for simulating an event is relatively uncommon, especially for a battlefield simulation. This paper presents a work-in-progress, specifically a web-based AR application that enables both students and tourists to witness a series of battlefield events occurring at the Battle of Palmito Ranch, located near Brownsville, Texas. With markers embedded directly into the printed map, users can experience the last battle of the Civil War in the US.

The fast-growing number of high-performance computer processor and hand-held devices have paved the way for the development of Virtual Reality and Augmented Reality in terms of hardware and software in the education sector. The question of whether students can adopt these new technologies is not fully addressed. Answering this question thus plays an essential role for instructors and course designers. The objectives of this study are: (1) to investigate the feasibility of the Virtual Reality/Augmented Reality development for undergraduate students, and (2) to highlight some practical challenges when creating and sharing Virtual Reality and Augmented Reality applications from student's perspective. Study design for the coursework was given with detail. During a 16-week long, 63 Virtual Reality/Augmented Reality applications were created from a variety of topics and various development tools. 43 survey questions are prepared and administered to students for each phase of the projects to address technical difficulties. The exploration method was used for data analysis.

In recent years, Unmanned Aerial Vehicle (UAV) has been used extensively in various applications from entertainment, virtual tourism to construction, mining, agriculture. Navigation, path planning, and image acquisition are the main tasks in administering these aerial devices in accordance with real-time object tracking for affordable aerial vehicles. Aircraft crash is one of the most critical issues due to the uncontrolled environment and signal loss that cause the aerial vehicle to hit the buildings on its returning mode. Furthermore, real-time image processing, such as object tracking, has not yet been exploited for a low-cost aerial vehicle. This paper proposes a prototype embedded in a Web-based application called DroneVR to mitigate the aforementioned issues. The virtual reality environment was reconstructed based on the real-world fly data (OpenStreetMap) in which path planning and navigation were carried out. Gaussian Mixture Model was used to extract foreground and detect a moving object, Kalman Filter method was then applied to predict and keep track of object's motion. Perceived ease of use was investigated with a small sample size users to improve the simulator.

With the advancement of modern technologies, Virtual Reality plays an essential role for training rescuers, particularly for disaster savers employing simulation training. By wholly immersed in the virtual environment, rescuers are capable of practicing the required skills without being threatened of their lives before experiencing the real world situation. This paper presented a work-in-progress Virtual Reality application called VRescuer to help trainees get used to various disaster circumstances. A scenario of a city was created with an ambulance rescuer and several rescuees in the scene. The intelligent ambulance rescuer was introduced as a rescuer/guider to automatically search and find the optimal paths for saving all rescuees. The trainee can interfere in the rescuing process by placing obstacles or adding more rescuees along the ways which cause the rescue agent to re-route the paths. The VRescuer was implemented in Unity3D with an Oculus Rift device, and it was assessed by twenty users to improve the proposed application.

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.

Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p &lt, 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 &deg, C to 5 &deg, C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 &deg, C in underwater environment, while estimation accuracy decreases as temperature goes down.

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.