Significantly more women than men in the United States studied abroad in the academic year of 2022/2023. In that year, about 67.2 percent of American students who were studying abroad were female.

At a time when OECD and partner countries are trying to figure out how to reduce burgeoning debt and make the most of shrinking public budgets, spending on education is an obvious target for scrutiny. Education officials, teachers, policy makers, parents and students struggle to determine the merits of shorter or longer school days or school years, how much time should be allotted to various subjects, and the usefulness of after-school lessons and independent study. This report focuses on how students use learning time, both in and out of school. What are the ideal conditions to ensure that students use their learning time efficiently? What can schools do to maximise the learning that occurs during the limited amount of time students spend in class? In what kinds of lessons does learning time reap the most benefits? And how can this be determined? The report draws on data from the 2006 cycle of the Programme of International Student Assessment (PISA) to describe differences across and within countries in how much time students spend studying different subjects, how much time they spend in different types of learning activities, how they allocate their learning time and how they perform academically.

Colleges and universities in the United States are still a popular study destination for Chinese students, with around 277 thousand choosing to take courses there in the 2023/24 academic year. Although numbers were heavily affected by the coronavirus pandemic, China is still the leading source of international students in the U.S. education market, accounting for 24.6 percent of all incoming students. The education exodus Mathematics and computer science courses led the field in terms of what Chinese students were studying in the United States, followed by engineering and business & management programs. The vast majority of Chinese students were self-funded, wth the remainder receiving state-funding to complete their overseas studies. Tuition fees can run into the tens of thousands of U.S. dollars, as foreign students usually pay out-of-state tuition fees. What about the local situation? Although studying abroad attracts many Chinese students, the country itself boasts the largest state-run education system in the world. With modernization of the national tertiary education system being a top priority for the Chinese government, the country has seen a significant increase in the number of local universities over the last decade. Enrolments in these universities exceeded 37 million in 2023, and a record of more than ten million students graduated in the same year, indicating that China's education market is still expanding.

The National Indian Education Study, 2009 (NIES 2009), is a study that is part of the National Indian Education Study (NIES), which is a part of National Assessment of Educational Progress (NAEP) program; program data is available since 2005 at https://nces.ed.gov/nationsreportcard/nies/. NIES 2009 (https://nces.ed.gov/nationsreportcard/nies/) is a cross-sectional survey that is designed to describe the condition of education for American Indian and Alaska Native (AI/AN) students in the United States. Students in public, private, Department of Defense, and Bureau of Indian Education-funded schools were sampled using paper-and-pencil assessment. Key statistics produced from NIES 2009 provides educators, policymakers, and the public with information about the academic performance in reading and mathematics of AI/AN fourth- and eighth-graders as well as their exposure to Native American culture.

List of student's achievement in the learning area of English by their year level and country of birth.

*This dataset is no longer being updated. For more information about Learning Outcomes go to https://qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes

BackgroundIn medical practice, clinically unexpected measurements might be quite properly handled by the remeasurement, removal, or reclassification of patients. If these habits are not prevented during clinical research, how much of each is needed to sway an entire study?Methods and ResultsBelieving there is a difference between groups, a well-intentioned clinician researcher addresses unexpected values. We tested how much removal, remeasurement, or reclassification of patients would be needed in most cases to turn an otherwise-neutral study positive. Remeasurement of 19 patients out of 200 per group was required to make most studies positive. Removal was more powerful: just 9 out of 200 was enough. Reclassification was most powerful, with 5 out of 200 enough. The larger the study, the smaller the proportion of patients needing to be manipulated to make the study positive: the percentages needed to be remeasured, removed, or reclassified fell from 45%, 20%, and 10% respectively for a 20 patient-per-group study, to 4%, 2%, and 1% for an 800 patient-per-group study. Dot-plots, but not bar-charts, make the perhaps-inadvertent manipulations visible. Detection is possible using statistical methods such as the Tadpole test.ConclusionsBehaviours necessary for clinical practice are destructive to clinical research. Even small amounts of selective remeasurement, removal, or reclassification can produce false positive results. Size matters: larger studies are proportionately more vulnerable. If observational studies permit selective unblinded enrolment, malleable classification, or selective remeasurement, then results are not credible. Clinical research is very vulnerable to “remeasurement, removal, and reclassification”, the 3 evil R's.

List of student's achievement in the learning area of Maths by SA2 and year level.

This dataset is no longer being updated. For more information about Learning Outcomes go to https://www.qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes

The National Education Longitudinal Study of 1988 (NELS:88) is a study that is part of the Longitudinal Studies Branch (LSB) program; program data is available since 1988 at . NELS:88 (https://nces.ed.gov/surveys/nels88/) is a longitudinal study that is designed to provide trend data about critical transitions experienced by students as they leave middle or junior high school, and progress through high school and into postsecondary institutions or the work force. A nationally representative sample of eighth-graders were first surveyed in the spring of 1988. A sample of these respondents were then resurveyed through four follow-ups in 1990, 1992, 1994, and 2000. Overall weighted response rate was unavailable as of December 2014. Key statistics produced from NELS:88 data can be used for policy-relevant research about educational processes and outcomes, for example: student learning; early and late predictors of dropping out; and school effects on students' access to programs and equal opportunity to learn.

The Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011), is part of the Early Childhood Longitudinal Study (ECLS) program; program data is available since 1998-99 at . ECLS-K:2011 is a longitudinal study that focuses on children's early school experiences beginning with kindergarten and continuing through the fifth grade. It includes the collection of data from parents, teachers, school administrators, and before- and after-school care providers, as well as direct child assessments of students. ECLS-K:2011 is exceptionally broad in its scope and coverage of child development, early learning, and school progress, drawing together information from multiple sources to provide rich data about the population of children who were kindergartners in the 2010-11 school year. The survey was conducted using computer-assisted interviews and paper questionnaires. The primary respondents in this study are students and their parents, general classroom teachers, special education teachers, school administrators, and before- and after-school care providers.

In 2024, about nine percent of all students who chose online degree programs in the United States said they did so because COVID-19 made it the only option available to them, a slight decrease from 11 percent in the previous year. In both 2023 and 2024, however, the most commonly cited reason for students to choose online degree programs was due to existing commitments, such as work and family, preventing their attendance in campus-based courses.

In a 2019 survey, 23 percent of grade 6-12 students reported that their teacher spent all of the class using digital learning technologies to teach in science, math, and history/social studies. All of these were significantly below computer science/information technology though, with 60 percent of surveyed students stating that they were taught using digital learning tools for the entire class.

Note the time teachers spend teaching a subject with digital tools was differentiated in the survey from the time students spent learning with digital learning tools. For almost all subjects, the time spent learning with digital tools was lower than the time spent teaching.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

Understanding Society (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society COVID-19 Study, 2020-2021 is a regular survey of households in the UK. The aim of the study is to enable research on the socio-economic and health consequences of the COVID-19 pandemic, in the short and long term. The surveys started in April 2020 and took place monthly until July 2020. From September 2020 they took place every other month until March 2021 and the final wave was fielded in September 2021. They complement the annual interviews of the Understanding Society study. The data can be linked to data on the same individuals from previous waves of the annual interviews (SN 6614) using the personal identifier pidp. However, the most recent pre-pandemic (2019) annual interviews for all respondents who have taken part in the COVID-19 Study are included as part of this data release. Please refer to the User Guide for further information on linking in this way and for geographical information options.

Latest edition information

For the eleventh edition (December 2021), revised April, May, June, July, September, November 2020, January 2021 and March 2021 data files for the adult survey have been deposited. These files have been amended to address issues identified during ongoing quality assurance activities. All documentation has been updated to explain the revisions, and users are advised to consult the documentation for details. In addition new data from the September 2021 web survey have been deposited.

Taiwan Number of Student: Applying for Visa to Study Abroad: Oceania data was reported at 6,313.000 Person in 2017. This records a decrease from the previous number of 7,265.000 Person for 2016. Taiwan Number of Student: Applying for Visa to Study Abroad: Oceania data is updated yearly, averaging 3,397.000 Person from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 7,322.000 Person in 2015 and a record low of 2,434.000 Person in 1998. Taiwan Number of Student: Applying for Visa to Study Abroad: Oceania data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under Global Database’s Taiwan – Table TW.G054: Education Statistics.

Employment income (in 2019 and 2020) by detailed major field of study and highest certificate, diploma or degree, including work activity (full time full year, part time full year, or part year).

The 2015 National Indian Education Study (NIES 2015) is a data collection that is part of the National Assessment of Educational Progress (NAEP) program; program data are available since 2005 at . NIES 2015 (https://nces.ed.gov/nationsreportcard/nies/) was a cross-sectional survey of the educational experiences of American Indian and Alaska Native (AI/AN) students in the United States. The data collection included paper-and-pencil questionnaires of students, as well as online questionnaires of teachers and school administrators. Key statistics produced from NIES 2015 include information on student knowledge of their Native cultures and languages; the extent to which AI/AN culture and language were a part of curricula; and the extent to which school resources were available for improving AI/AN student achievement.

List of student's achievement in the learning area of Maths by gender and year level.

This dataset is no longer being updated. For more information about Learning Outcomes go to https://qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes

Latest monthly statistics on Learning Disabilities and Autism (LDA) patients from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). Data on inpatients with learning disabilities and/or autism are being collected both within the AT collection and MHSDS. There are differences in the inpatient figures between the AT and MHSDS data sets and work has been ongoing to better understand these. LDA data from MHSDS are experimental statistics, however, while impacts from the cyber incident are still present they will be considered to be management information. From October 2021, LDA MHSDS data has been collected under MHSDS version 5. From 1 July 2022, Integrated Care Boards were established within Integrated Care Systems data and replaced Sustainability and Transformation Plans (STPs). Clinical Commissioning Groups have been replaced by sub-Integrated Care Boards. Data for the AT collection is now submitted by sub-Integrated Care Boards. This has resulted in some renaming within tables and the inclusion of a new Table 5.1b with a patient breakdown by submitting organisation. Patients by originating organisation and commissioning type are still available in Table 5.1a. Data in the tables are now presented by the current organisational structures. Old organisational structures have been mapped to new structures in any time series. As of 20th June 2024, restraints data for MHSDS March 2024 has been added to the 'Learning disability services monthly statistics from MHSDS: Data tables' page. This is available within Tables 15-18 of v2 of the Data tables as well as within v2 of the csv file.

List of student's achievement in the learning area of English by Indigenous status and year level.

This dataset is no longer being updated. For more information about Learning Outcomes go to https://www.qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes