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This article begins with a historical overview of the STEM education policy and related classroom practices in Hong Kong. Against the backdrop that the Hong Kong education authority has been promoting STEM education over the past few years, there has been a pressing need for effective strategies of implementing STEM education in the context of reduced class size classrooms (i.e. small class teaching). With a view to addressing this need, this article strives to exemplify the incorporation of STEM education into small class teaching settings. Intended as a conceptual paper drawing on design approaches for STEM lessons and a small class teaching framework, the article demonstrates the relevance of STEM education to small class teaching. Two illustrative lesson design examples are provided to showcase how the design approaches and framework can be operationalised. In addition, the article also offers a thoughtful discussion concerning the potential challenges of delivering the two sample lessons as well as the coping strategies. It contributes to understanding of STEM education theories and provides a valuable reference for educational practitioners.

This paper provides a long-term follow-up analysis of students who participated in the Tennessee STAR experiment. In this experiment, students and their teachers were randomly assigned to small, regular-size, or regular-size classes with a teacher aide in the first four years of school. We analyse the effect of past attendance in small classes on student test scores and whether they took the ACT or SAT college entrance exam. Attending a small class in the early grades is associated with an increased likelihood of taking a college-entrance exam, especially among minority students, and somewhat higher test scores.

I consider nonparametric identification of nonseparable instrumental variables models with continuous endogenous variables. If both the outcome and first stage equations are strictly increasing in a scalar unobservable, then many kinds of continuous, discrete, and even binary instruments can be used to point-identify the levels of the outcome equation. This contrasts sharply with related work by Imbens and Newey (2009) that requires continuous instruments with large support. One implication is that assumptions about the dimension of heterogeneity can provide nonparametric point-identification of the distribution of treatment response for a continuous treatment in a randomized controlled experiment with partial compliance.

Performance gaps in science are well documented, and an examination of underlying mechanisms that lead to underperformance and attrition of women and underrepresented minorities (URM) may offer highly targeted means to promote such students. Determining factors that influence academic performance may provide a basis for improved pedagogy and policy development at the university level. We examined the impact of class size on students in 17 biology courses at four universities. Although the female students underperformed on high-stakes exams compared with the men as class size increased, the women received higher scores than the men on nonexam assessments. The URM students underperformed across grade measures compared with the majority students regardless of class size, suggesting that other characteristics of the education environment affect learning. Student enrollment is expected to increase precipitously in the next decade, underscoring the need to prioritize individual student potential rather than yield to budget constraints when considering equitable pedagogy and caps on classroom sizes.

Attracting and retaining teachers can be an important ingredient in improving low-performing schools. In this study, we estimate the expressed preferences for teachers who have worked in low-performing schools in Tennessee. Using adaptive conjoint analysis survey design, we examine three types of school attributes that may influence teachers’ employment decisions: fixed school characteristics, structural features of employment, and malleable school processes. We find that teachers express a strong preference for two malleable school processes, administrative support and discipline enforcement, along with a higher salary, a structural feature. Estimates indicate these attributes are 2 to 3 times more important to teachers than fixed school characteristics like prior achievement. We validate our results using administrative data on teachers’ revealed preferences.

This paper examines the effect of early childhood investments on college enrollment and degree completion. We used the random assignment in Project STAR (the Tennessee Student/Teacher Achievement Ratio experiment) to estimate the effect of smaller classes in primary school on college entry, college choice, and degree completion. We improve on existing work in this area with unusually detailed data on college enrollment spells and the previously unexplored outcome of college degree completion. We found that assignment to a small class increases students' probability of attending college by 2.7 percentage points, with effects more than twice as large among black students. Among students enrolled in the poorest third of schools, the effect is 7.3 percentage points. Smaller classes increased the likelihood of earning a college degree by 1.6 percentage points and shifted students toward high-earning fields such as STEM (science, technology, engineering, and mathematics), business, and economics. We found that test-score effects at the time of the experiment were an excellent predictor of long-term improvements in postsecondary outcomes.

Kenyon College, a highly selective, nationally ranked liberal arts college in central Ohio, invites applications for the Marilyn Yarbrough Dissertation/Teaching Fellowship beginning in July 2023.

In this meta-analysis, we synthesize the results of 24 studies using the Colorado Learning Attitudes about Science Survey (CLASS) and the Maryland Physics Expectations Survey (MPEX) to answer several questions: (1) How does physics instruction impact students’ beliefs? (2) When do physics majors develop expert-like beliefs? and (3) How do students’ beliefs impact their learning of physics? We report that in typical physics classes, students’ beliefs deteriorate or at best stay the same. There are a few types of interventions, including an explicit focus on model-building and (or) developing expertlike beliefs that lead to significant improvements in beliefs. Further, small courses and those for elementary education and nonscience majors also result in improved beliefs. However, because the available data oversamples certain types of classes, it is unclear whether these improvements are actually due to the interventions, or due to the small class size, or student populations typical of the kinds of classes in which these interventions are most often used. Physics majors tend to enter their undergraduate education with more expertlike beliefs than nonmajors and these beliefs remain relatively stable throughout their undergraduate careers. Thus, typical physics courses appear to be selecting students who already have strong beliefs, rather than supporting students in developing strong beliefs. There is a small correlation between students’ incoming beliefs about physics and their gains on conceptual mechanics surveys. This suggests that students with more expertlike incoming beliefs may learn more in their physics courses, but this finding should be further explored and replicated. Some unanswered questions remain. To answer these questions, we advocate several specific types of future studies: measuring students’ beliefs in courses with a wider range of class sizes, student populations, and teaching methods, especially large classes with very innovative pedagogy and small classes with more typical pedagogy; analysis of the relationship between students’ beliefs and conceptual understanding including a wide variety of variables that might influence each; and analysis of large data sets from a variety of classes that track individual students rather than averaging over classes.

This paper analyzes data on 11,600 students and their teachers who were randomly assigned to different size classes from kindergarten through third grade. Statistical methods are used to adjust for nonrandom attrition and transitions between classes. The main conclusions are (1) on average, performance on standardized tests increases by four percentile points the first year students attend small classes; (2) the test score advantage of students in small classes expands by about one percentile point per year in subsequent years; (3) teacher aides and measured teacher characteristics have little effect; (4) class size has a larger effect for minority students and those on free lunch; (5) Hawthorne effects were unlikely.

Agent-based modeling and network analysis can help integrate knowledge on “micro-level” mechanisms and “macro-level” effects. Education researchers have struggled for decades with questions such as “why are troubled schools so difficult to improve?” or “why is the achievement gap so hard to close?” We argue here that conceptualizing schools and districts as complex adaptive systems, composed of many networked parts that give rise to emergent patterns through their interactions ( 1 ), holds promise for understanding such important problems. Although there has been considerable research on the use of complex systems ideas and methods to help students learn science content ( 2 ), only recently have researchers begun to apply these tools to issues of educational policy.