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This article analyzes cross-national trends in national student-faculty ratios (SFRs) over the past five decades. In descriptive analyses, we find that SFRs have increased globally, driven by particularly large increases in low-income countries. We analyze two cross-national datasets to examine factors associated with national SFRs. We find that national SFRs are positively associated with gross tertiary enrollment rates and particularly so in low-income countries. In contrast, both the female share of faculty and research spending are associated with having lower national SFRs. The findings shed light on how national higher education systems are responding to massification pressures and suggest that differentiating faculty roles is one way that countries curb their rising SFRs as enrollments grow.

Theory suggests that teachers’ implicit racial attitudes affect their students, but large-scale evidence on U.S. teachers’ implicit biases and their correlates is lacking. Using nationwide data from Project Implicit, we found that teachers’ implicit White/Black biases (as measured by the implicit association test) vary by teacher gender and race. Teachers’ adjusted bias levels are lower in counties with larger shares of Black students. In the aggregate, counties in which teachers hold higher levels of implicit and explicit racial bias have larger adjusted White/Black test score inequalities and White/Black suspension disparities.

This study uses data from a comprehensive database of U.S. early childhood education program evaluations published between 1960 and 2007 to evaluate the relationship between class size, child-teacher ratio, and program effect sizes for cognitive, achievement, and socioemotional outcomes. Both class size and child–teacher ratio showed nonlinear relationships with cognitive and achievement effect sizes. For child–teacher ratios 7.5:1 and lower, the reduction of this ratio by one child per teacher predicted an effect size of 0.22 standard deviations greater. For class sizes 15 and smaller, one child fewer predicted an effect size of 0.10 standard deviations larger. No discernible relationship was found for larger class sizes and child–teacher ratios. Results were less clear for socioemotional outcomes due to a small sample.

For over a decade, Immigration and Customs Enforcement (ICE) has formed partnerships allowing local police to enforce immigration law by identifying and arresting undocumented residents. Prior studies, using survey data with self-reported immigrant and citizenship status, provide mixed evidence on their demographic impact. This study presents new evidence based on Hispanic public school enrollment. We find local ICE partnerships reduce the number of Hispanic students by 10% within 2 years. We estimate partnerships enacted before 2012 displaced more than 300,000 Hispanic students. These effects are concentrated among elementary school students. We find no corresponding effects on the enrollment of non-Hispanic students and no evidence that ICE partnerships reduced pupil-teacher ratios or the percentage of students eligible for the National School Lunch Program.

A policy change is used to estimate the effect of teacher density on student performance. We find that an increase in teacher density has a positive effect on student achievement. The baseline estimate-obtained by using the grade-point average as the outcome variable-implies that resource increases corresponding to the class-size reduction in the STAR experiment (a reduction of seven students) improves performance by 2.6 percentile ranks (or 0.08 standard deviations). When we used test-score data for men, potentially a more objective measure of student performance, the effect of resources appears to be twice as large.

Research suggests a positive relationship between schools' efforts to engage parents and parents' involvement in their child's education. The authors investigated school socioeconomic status, school size, grade level, and student-teacher ratio as predictors of schools' efforts to engage parents of students receiving special education services. The dependent variable was the Schools' Efforts to Partner with Parents' Scale, which has been validated for states' use in their federal accountability systems. Mean school-level scores were calculated for 265 schools in a large southeastern state. Results indicated that student-teacher ratio was the strongest predictor of parents' perceived school engagement efforts. Implications are drawn for ways in which all schools, including those with high student-teacher ratios, can improve their collaboration with parents of students receiving special education services.

In massive open online learning courses (MOOCs) with a low instructor-student ratio, students are expected to have self-directed learning abilities. This study investigated the relationship among motivation, self-monitoring, self-management, and MOOC learners’ use of learning strategies. An online survey was embedded at the end of three MOOCs with large enrollments asking for learners’ voluntary participation in the study. The survey results from 470 participants indicated that motivation positively influenced self-monitoring, self-management, and learning strategies. In addition, self-monitoring and self-management did not affect the utilization of learning strategies. This underscores learners’ motivation and the need to encourage them to adopt appropriate learning strategies for successful learning. The results also revealed that self-monitoring positively affected self-management. The findings highlight the critical need to enhance self-monitoring skills to further promote self-management skills in MOOCs. In addition, self-monitoring and self-management did not encourage learners to use related learning strategies in this study. This study should be extended to investigate practical ways to encourage MOOC learners to adopt learning strategies.

Background This study aimed to answer the research question: How reliable is ChatGPT in automated essay scoring (AES) for oral and maxillofacial surgery (OMS) examinations for dental undergraduate students compared to human assessors? Methods Sixty-nine undergraduate dental students participated in a closed-book examination comprising two essays at the National University of Singapore. Using pre-created assessment rubrics, three assessors independently performed manual essay scoring, while one separate assessor performed AES using ChatGPT (GPT-4). Data analyses were performed using the intraclass correlation coefficient and Cronbach's α to evaluate the reliability and inter-rater agreement of the test scores among all assessors. The mean scores of manual versus automated scoring were evaluated for similarity and correlations. Results A strong correlation was observed for Question 1 ( r  = 0.752–0.848, p  < 0.001) and a moderate correlation was observed between AES and all manual scorers for Question 2 ( r  = 0.527–0.571, p  < 0.001). Intraclass correlation coefficients of 0.794–0.858 indicated excellent inter-rater agreement, and Cronbach’s α of 0.881–0.932 indicated high reliability. For Question 1, the mean AES scores were similar to those for manual scoring ( p  > 0.05), and there was a strong correlation between AES and manual scores ( r  = 0.829, p  < 0.001). For Question 2, AES scores were significantly lower than manual scores ( p  < 0.001), and there was a moderate correlation between AES and manual scores ( r  = 0.599, p  < 0.001). Conclusion This study shows the potential of ChatGPT for essay marking. However, an appropriate rubric design is essential for optimal reliability. With further validation, the ChatGPT has the potential to aid students in self-assessment or large-scale marking automated processes.

Community colleges have been under pressure for years to improve retention rates. Considering well-publicized reductions in state funding during and after the Great Recession, progress in this area is unexpected. And yet this is precisely what we find. Using the Integrated Postsecondary Education Data System (IPEDS), we find an average increase in retention of nearly 5 percentage points, or 9 percent, across the sector from 2004 to 2017. Over 70% of institutions posted retention gains, and average improvement occurred yearly over the period excepting a reversal at the height of the Great Recession. Gains were smaller on average at schools with higher tuition and that serve more disadvantaged populations, and larger at institutions with lower student-faculty ratios and higher per-student instructional spending. Fixed-effects regression and Oaxaca decomposition analyses demonstrate that these gains were not caused by observable changes in student body composition or in institutional characteristics such as increased per-student instructional spending.

This paper presents new evidence on how enrollment expansion affects higher education access and production with a focus on social inequality and institutional stratification. From 1999 to 2012, the world’s largest higher education expansion happened in China that annual college enrollment dramatically increased from 1,083,600 to 6,888,300. We evaluate this exogenous, unprecedented policy using nationally representative student-level survey data and newly available confidential institution-level data. Enrollment expansion, which reduced per-student resources, negatively impacted college quality as measured by value-added on graduates’ employment and earnings. The inequality in access between high- and low-SES students and the stratified production between college institutional tiers persisted during expansion.