The literature indicates that few studies have been conducted with persons with visual impairments (that is, those who are blind or have low vision) concerning mobile application or “app” usage. The current study explores the use of mobile apps with this population globally. A total of 259 participants with visual impairments completed an online survey. Descriptive statistics and bivariate tests were used to examine associations between demographic characteristics and mobile app use. The participants rated special apps as useful (95.4%) and accessible (91.1%) tools for individuals with visual impairments. More than 90% of the middle-aged adult group strongly agreed with the practicality of special apps, a significantly higher percentage than was observed in the young and old adult groups. In addition, the participants with low vision considered special apps less accessible than did those with blindness (p < .05). Results show that persons with visual impairments frequently use apps specifically designed for them to accomplish daily activities. Furthermore, this population is satisfied with mobile apps and would like to see improvements and new apps. Developers of apps for individuals with visual impairments need to refine and test the existing apps. Practitioners need to be knowledgeable about app usage so they can provide effective instruction to their students or clients. This study provides preliminary information regarding app usage among persons with visual impairments.

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.

This study examines the degree of macroeconomic recovery of the Nepal tourism industry after a natural disaster using the autoregressive integrated moving average model (ARIMA). The study investigated the case of Nepal’s earthquakes in 2015 and examined the impact of the earthquakes on tourism inflows and GDP using time series data from 1990 to 2018. The results show that the increasing trend in the number of tourists changes in the post-earthquake period. In particular, the excess in tourist demand by age and purpose of visits after the earthquake indicates natural disaster as a potential reason for a tourism demand boost, often described as dark tourism in literature. This research shows the process of a heritage tourist destination assessing macroeconomic recovery from a natural disaster and fills the gap in the literature regarding purpose-based tourism demand and a link between dark tourism and disaster recovery on a heritage tourism destination.

Research in education and developmental psychology indicates that behavioral engagement in learning is a critical predictor of children’s academic success. In an effort to improve academic achievement, school administrators are continually in search of methods to increase behavioral engagement. Previous research has indicated that classroom-based physical activity (PA) lessons have a positive impact on academic achievement. However, little research has been done in assessing the impact of such interventions on the behavioral engagement of students with learning behavior difficulties. This study assesses the impact of classroom-based PA on teacher-rated classroom behaviors of students with identified learning behavior difficulties. Two schools (one intervention, one control) participating in a larger, cluster-randomized trial provided scores on a teacher-administered classroom behavior scale. This scale was used to collect information on 15 characteristics identified as being essential to behavioral engagement. Participants included male and female students in second and third grade classrooms who were identified by their classroom teacher and school counselor as having difficulties with learning behaviors. Mixed linear modeling for repeated measures was used to examine the changes over time in the classroom behavior scores. The intervention group showed significant improvement over time in classroom behavior while the control group showed no change or a slight degradation over time (i.e., group × time interaction, F[2132] = 4.52, p = 0.01). Schools must meet the diverse needs of students today, including those who exhibit less than optimal learning behaviors. Combined with the evidence that PA is linked to several health and cognitive-behavior benefits, providing classroom-based PA that is incorporated within the curriculum provides common ground for all students to participate. It is a potential solution to increasing behavioral engagement, and in turn stimulating and enhancing learning.

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.