Educational Research for Policy and Practice最新文献

This is an era of uncertainty, during which adaptability is a key capability to survival and future success. What has Singapore done to develop an education system that facilitates its young to learn in such an era? Firstly, Singapore enhances the adaptive capacity of its education system by increasing its diversity. In particular, it is developing more educational pathways, areas of learning and types of schools. Secondly, Singapore emphasizes lifelong learning. In particular, Singapore will be focusing on providing more learning opportunities for working adults and enhancing support for mid-career reskilling. In these educational changes, Singapore has been addressing both hardware (changing policies) and software (addressing culture), in order to develop an education system that can facilitate learners to learn in an era of uncertainty. What will help Singapore in further developing such a system? This paper suggests two software "upgrades", namely "inspiration by design" and "readiness for paradigm shift". Firstly, instead of a paradigm of competence, Singapore adopts a paradigm of inspiration. In this paradigm, the aim is for students to find their inspiration to learn, and such inspiration is provided through a systemic approach. This is "inspiration by design". Secondly, Singapore nurtures the spirit of readiness for paradigm shifts in education. An example of a paradigm shift is to be ready for the advent of artificial intelligence in education. Such readiness is about reflecting on fundamentals so that the use of artificial intelligence enhances the development of real human intelligence.

Studies have shown that the digital divide affects students' educational achievement across racial and ethnic groups. In light of this, the study investigates the effect of technology access at home on student learning hours during the COVID-19 pandemic and across racial and ethnic groups in the U.S. The Household Pulse Surveys (HPS), conducted by the United States Census Bureau and administered from August 19, 2020, to March 29, 2021, were used for the analysis. We compute a composite index of technology access using the principal component analysis (PCA). And for the empirical model, the study employed a Tobit regression model. The result shows that the estimated index of technology access based on PCA for the whole sample is about 0.92, indicating a higher level of access. However, the breakdown by race/ethnicity shows an average of about 0.93, 0.89, 0.90, 0.94, and 0.89 for students representing White, Black, Hispanic, Asia, and other races, respectively. This means the intensity at which households in the sample have access to technology is higher among the Asian and White students, followed by Hispanic, Black, and other races in that order. The estimated effect of technology access on the student learning hours during COVID-19 based on the Tobit regression model shows about a 3.1 unit points increase over the whole sample. And further analysis reveals variation at which access to the technology impacts learning hours across race and ethnicity groups. For example, we find that access to technology significantly increased learning hours by about 3.5, 1.6, 2.2, and 3.4 unit points among White, Black, Hispanic, and Asian students, respectively. The observed differing effect of access to technology on learning hours further highlights the racial disparities in American society's digital divide, which reveal how access to technology disproportionately impacts student learning hours during the COVID-19 pandemic across race and ethnicity.