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Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO 2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy ( R 2 : 0.388–0.484) and had the worst direct ( R 2 : 0.0001–0.349) and indirect transferability ( R 2 : 0.07–0.352). Those models using the traditional method had the highest model fitting R 2 (0.629–0.966) with lower cross-validation R 2 (0.495–0.941) and slightly better direct transferability ( R 2 : 0.0003–0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R 2 (0.601–0.966) and the best cross-validation R 2 (0.514–0.941). They also had the strongest direct transferability ( R 2 : 0.006–0.590) and moderate-to-good indirect transferability ( R 2 : 0.072–0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.

As teachers returned to the classroom for the 2020-2021 school year, they faced new and challenging environments, instructional approaches, and roles as educators. The current study is one of the first empirical studies that identified factors contributing to teacher burnout due to COVID-19 (coronavirus disease) and instruction during fall 2020. Controlling for demographics, the results found significant predictors for teacher burnout-stress those being COVID-19 anxiety, current teaching anxiety, anxiety communicating with parents, and administrative support. The results are important for schools and researchers to consider when it comes to the impact of COVID-19 on teachers.

Second language (L2) anxiety has been the object of constant empirical and theoretical attention for several decades. As a matter of both theoretical and practical interest, much of the research in this domain has examined the relationship between anxiety and L2 achievement. The present study meta-analyzes this body of research. Following a comprehensive search, a sample of 97 reports were identified, contributing a total of 105 independent samples (N = 19,933) from 23 countries. In the aggregate, the 216 effect sizes (i.e., correlations) reported in the primary studies yielded a mean of r = −.36 for the relationship between L2 anxiety and language achievement. Moderator analyses revealed effects sizes to vary across different types of language achievement measures, educational levels, target languages, and anxiety types. Overall, this study provides firm evidence for both the negative role of L2 anxiety in L2 learning and the moderating effects of a number of (non)linguistic variables. We discuss the findings in the context of theoretical and practical concerns, and we provide direction for future research.

As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education context. In addition, what AI algorithms are commonly used and how they influence online higher education remain unclear. To fill these gaps, this systematic review provides an overview of empirical research on the applications of AI in online higher education. Specifically, this literature review examines the functions of AI in empirical researches, the algorithms used in empirical researches and the effects and implications generated by empirical research. According to the screening criteria, out of the 434 initially identified articles for the period between 2011 and 2020, 32 articles are included for the final synthesis. Results find that: (1) the functions of AI applications in online higher education include prediction of learning status, performance or satisfaction, resource recommendation, automatic assessment, and improvement of learning experience; (2) traditional AI technologies are commonly adopted while more advanced techniques (e.g., genetic algorithm, deep learning) are rarely used yet; and (3) effects generated by AI applications include a high quality of AI-enabled prediction with multiple input variables, a high quality of AI-enabled recommendations based on student characteristics, an improvement of students’ academic performance, and an improvement of online engagement and participation. This systematic review proposes the following theoretical, technological, and practical implications: (1) the integration of educational and learning theories into AI-enabled online learning; (2) the adoption of advanced AI technologies to collect and analyze real-time process data; and (3) the implementation of more empirical research to test actual effects of AI applications in online higher education.

Objective: To determine which variables have the ability to predict hand grip strength in Quilombola children and adolescents. Methods: We calculated the sample seeking an R2 between 0.1 and 0.2 for a single dependent variable (handgrip strength), with 6 predictor variables (age, body mass, stature, BMI, fat, and lean mass), alpha of 0.05 and beta of 0.80; and included children and adolescents between the ages of 6 and 17 (n=82). We measured handgrip strength using the Jamar dynamometer and built a model and evaluated the association between the predictor variables (i.e., independent, x-axis) and the outcome variable (i.e., dependent, y-axis [dynamometry]) by analysis of variance of mathematically adjusted models (F-value >60, p <0.05). Results: We noted an increasing gain in strength over the years, although between the ages of 11 and 12 and between 13 and 14, there was an apparent loss of strength on the part of Quilombola adolescents, passing from 18.75 to 16.12 and from 23.5 to 19.83, respectively. We observed that the variables age, stature, and lean mass contributed significantly (p <0.05, β coefficient ranging from 3.050 to 3.844) to the performance of the built model (F [7.74] = 62.16, p <0.001; R2 = 0.84). Conclusion: Age, stature, and lean mass significantly contribute to the performance of the built model. Namely, 84% of the variation in the mean handgrip strength may be explained by the independent variables. Therefore, the predicted handgrip strength, in kg, corresponds to: -29.530 + 1.103 + 0.196 + 0.011 × (age [years] + stature [cm] + lean mass [kg]).

This meta-analysis reviewed 82 school-based, universal social and emotional learning (SEL) interventions involving 97,406 kindergarten to high school students (Mage = 11.09 years; mean percent low socioeconomic status = 41.1; mean percent students of color = 45.9). Thirty-eight interventions took place outside the United States. Follow-up outcomes (collected 6 months to 18 years postintervention) demonstrate SEL's enhancement of positive youth development. Participants fared significantly better than controls in social-emotional skills, attitudes, and indicators of well-being. Benefits were similar regardless of students' race, socioeconomic background, or school location. Postintervention social-emotional skill development was the strongest predictor of well-being at follow-up. Infrequently assessed but notable outcomes (e.g., graduation and safe sexual behaviors) illustrate SEL's improvement of critical aspects of students' developmental trajectories.

Listening comprehension and word decoding are the two major determinants of the development of reading comprehension. The relative importance of different language skills for the development of listening and reading comprehension remains unclear. In this 5-year longitudinal study, starting at age 7.5 years (n = 198), it was found that the shared variance between vocabulary, grammar, verbal working memory, and inference skills was a powerful longitudinal predictor of variations in both listening and reading comprehension. In line with the simple view of reading, listening comprehension, and word decoding, together with their interaction and curvilinear effects, explains almost all (96%) variation in early reading comprehension skills. Additionally, listening comprehension was a predictor of both the early and later growth of reading comprehension skills.

Educational researchers and practitioners assert that supportive school and classroom climates can positively influence the academic outcomes of students, thus potentially reducing academic achievement gaps between students and schools of different socioeconomic status (SES) backgrounds. Nonetheless, scientific evidence establishing directional links and mechanisms between SES, school climate, and academic performance is inconclusive. This comprehensive review of studies dating back to the year 2000 examined whether a positive climate can successfully disrupt the associations between low SES and poor academic achievement. Positive climate was found to mitigate the negative contribution of weak SES background on academic achievement; however, most studies do not provide a basis for deducing a directional influence and causal relations. Additional research is encouraged to establish the nature of impact positive climate has on academic achievement and a multifaceted body of knowledge regarding the multilevel climate dimensions related to academic achievement.

Informed by the theoretical lens of opportunity hoarding, this study considers whether STEM postsecondary fields stand apart via the disproportionate exclusion of Black and Latina/o youth. Utilizing national data from the Beginning Postsecondary Study (BPS), the authors investigate whether Black and Latina/o youth who begin college as STEM majors are more likely to depart than their White peers, either by switching fields or by leaving college without a degree, and whether patterns of departure in STEM fields differ from those in non-STEM fields. Results reveal evidence of persistent racial/ethnic inequality in STEM degree attainment not found in other fields.

Mental health difficulties are highly prevalent in individuals on the autism spectrum. The current study examined how experiences and perceptions of autism acceptance could impact on the mental health of autistic adults. 111 adults on the autism spectrum completed an online survey examining their experiences of autism acceptance, along with symptoms of depression, anxiety and stress. Regression analyses showed that autism acceptance from external sources and personal acceptance significantly predicted depression. Acceptance from others also significantly predicted stress but acceptance did not predict anxiety. Further analyses suggested that experiences of “camouflaging” could relate to higher rates of depression. The current study highlights the importance of considering how autism acceptance could contribute to mental health in autism.