Asia Pacific Education Review最新文献

The Chinese government has been actively recruiting foreign-trained Chinese scholars to return to China since the Chinese brain drain began. Japan is among the most popular destinations for Chinese scholars seeking to receive doctoral training. This study explores the factors contributing to the stratification of Japanese-trained Chinese PhDs’ academic career attainments using the Mertonian norm of universalism. The results indicate that the norm of universalism can partly explain the stratification of Japanese-trained Chinese PhDs. The reason for this is that their higher pre-graduation productivity enhances the chance that Japanese-trained Chinese PhDs have of obtaining an academic position at a top university in China. In addition to pre-graduation academic productivity, other factors, including the prestige of the university attended, the duration of the academic sojourn in Japan, and the ethnicity of the supervisor influence employment outcomes.

This study aims to determine the changes in the career barrier (CB) trajectory of South Korean school dropouts over time and to identify the number of groups that can be categorized according to CB trajectory. The study analyzed three years of panel data on school dropouts from the Korean National Youth Policy Institute, which comprises adolescents in middle (ages 12–14 years) and high (ages 14–17 years) school. A total of 656 adolescents participated and were composed of 385 men (58.7%) and 271 women (41.3%). We used the latent growth model and conducted latent class growth analysis to classify CB trajectories and logistic regression to identify the influencing factors. Specifically, 6.6%, 15.7%, 22.9%, and 54.9% of the participants were classified into four groups, namely, ‘low level-increase,’ ‘low level-decrease,’ ‘high level-increase,’ and ‘middle level-maintained.’ Self-esteem, parental attachment, life satisfaction, depression, and career search behavior significantly influenced the classification of the groups. Lastly, the study discussed the theoretical and clinical implications and limitations.

Longitudinal item response data often exhibit two types of measurement noninvariance: the noninvariance of item parameters between subject groups and that of item parameters across multiple time points. This study proposes a comprehensive approach to the simultaneous modeling of both types of measurement noninvariance in terms of longitudinal item response modeling while incorporating a latent growth curve model. The growth curve model represents the growth trajectory using initial status and (non)linear change over multiple time points. In addition, this study investigates the impact of measurement noninvariance across time points and subject groups in the examination of group differences in growth over multiple time points. To demonstrate the efficacy of the proposed model, an empirical application to large-scale longitudinal survey data is provided, along with the results of a simulation study.

This research investigates access to senior school science and mathematics subjects offered in the final year of secondary schooling. Using data from the most populous Australian state of New South Wales, we examine whether stratification occurs in access to science and mathematics curricula. We find that the opportunity to study these subjects differs by key school characteristics, including location, socioeconomic composition and school sector. We find that while some science subjects and entry level mathematics are offered in most schools, substantial inequalities exist in access to the most advanced level of mathematics and chemistry. School location, socioeconomic composition, enrolment size and the availability of teachers predict the probability of whether a school offers the least and most advanced science and mathematics subjects. The findings highlight that stratification in curricula offerings occurs systemically and may intensify educational inequalities.

Industrial Revolution 4.0 is fundamentally changing the economy, the labor market, and the requirements for training professionals. The Concept of Education 4.0 and the Model of the University 4.0 link the requirements of the new industry and the information economy with the development and transformation of education. The study purpose is to identify areas for growth and areas of research excellence in the field of university readiness for the transition of the University 4.0 Model. The study is based on the method of systematic review. The study sample included initially 8389 research articles from the Scopus, World of Science, and Google Scholar databases, from which duplicate sources were filtered and 128 articles relevant to the research area of knowledge were selected. As a result of the algorithmic and human-assisted identification of the main research domains, it was found that the transformation of education toward University 4.0 is actively developing and has reached a peak in terms of the number of publications in 2022; discovery of significant differences in the processes of transformation of university education in different countries and the manifestation of special forms of digital inequality; the crucial role of distance learning and the formation of a digital environment for the transformation toward the University Model 4.0; and the greatest attention to the level of readiness of students and teachers to the processes of transformation of education. The significance and novelty of the study lies in closing the research gap of reviews of this field of research and predicting further research.

As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their inception, propensity scores have made an important contribution to the improvement in estimation of causal impacts, particularly in the absence of randomization. When certain core assumptions hold, propensity score-based methods allow for bias-reduced estimation of average treatment effects. In addition to their important role in causal studies, propensity scores have also been integral in improving generalizations from causal studies, specifically when study samples are not randomly selected from a target population of inference. The current study provides an overview of propensity scores, a discussion of the assumptions needed to ensure their validity, and an illustration of the methods both for causal inference and generalization. We highlight the importance of propensity score methods and discuss current applications and directions for ongoing and future research.

The use of automated feedback (AF) has been increasing in English writing courses for emergency remote education (ERE) due to the escalating COVID-19 pandemic crisis in Vietnam. The ERE English writing courses for in-service bachelor programs demanded an effective, fast, free, and user-friendly AF tool that does not require a login ID, which can help students to develop English writing skills and increase their motivation and self-learning ability. The main objectives of Vietnamese in-service students were to improve their English writing skills and update teaching methodologies that integrate technology. Although many studies emphasized the impact of AF tools in normal teaching contexts, relatively less research was conducted the use of AF tools among in-service students. This study examined the contribution of the Marking Mate AF to the improvement of the English writing skills of in-service learners in ERE writing classes. The participants were 82 in-service students in a Vietnamese public university. The dataset included English writing pre and posttest results collected during the first semester of academic year 2020–2021. The mean scores (standard deviation) of these tests were used to benchmark the writing performance of students. A self-report survey was also conducted to investigate the attitude of students toward Marking Mate AF. The results demonstrated that the posttest score (8.739; SD = 0.8495) was higher than the pretest score (7.439; SD = 0.976). Students expressed favorable opinions toward the usefulness of Marking Mate. Based on the findings, the study discussed the pedagogical implications regarding the incorporation of AF in face-to-face and online classes.

Computational thinking is becoming an essential skill in educational systems. There is, however, still a great deal to learn about it. This research aims to evaluate whether the inclusion of a specific material (Junior School Bebras Cards) favors the development of primary students’ computational thinking skills. For this purpose, 37 fourth-grade students participated in a quasi-experimental study to test the effectiveness of this material and its implementation using gamification. Three research questions were evaluated: the development of computational thinking skills, the influence of gamification on learning outcomes, and the influence of gamification on student motivation. The intervention was designed using the Junior School Bebras Cards, an unplugged (no technology) material proposed by UK Bebras. Two groups worked with these materials: an experimental group that received gamified instruction and a control group that received nongamified instruction. The three research questions were assessed through the comparative analyses of tests performed before and after the intervention. The results reveal that using Junior School Bebras Cards is beneficial for developing computational thinking skills; however, gamification using this material did not improve students’ learning outcomes or motivation.