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This study investigates how individual states raise revenue to pay for elementary-secondary education spending after a school finance reform (SFR). We consider 24 states that implemented SFRs between 1989 and 2005. Using a synthetic control approach, we identify six case-study states (Arkansas, Kansas, Maryland, Michigan, New Hampshire, and Vermont) that increased and sustained education expenditures after reform. We then searched for legislative statutes that appropriated funding for increased education spending and identified how policymakers intended to fund the SFR. Five states—AK, KS, MI, NH, and VT—paid for increased education expenditures by altering tax rates and changing tax revenue sources. A common feature among these five states is that they increased their control over the management of property tax revenues.
Field supervisors are central to clinical teaching, but little is known about how their feedback informs preservice teachers (PSTs) development. This sequential mixed methods study examines over 3,000 supervisor observation evaluations. We qualitatively code supervisor written feedback, which indicates 2 broad pedagogical categories and 9 separate skills. We then quantize these feedback codes to identify the variation in the presence of these codes across PST characteristics, and then use several modeling techniques to indicate that specific feedback codes are negatively associated with evaluation score. Managing student attention was most detrimental to scores in early observations whereas instructional feedback (e.g., lesson delivery) was prioritized later in clinical teaching. Findings inform teacher preparation policy on understanding PST development and improving supervisory feedback.
“Grow Your Own” (GYO) programs have recently emerged as a promising approach to expand teacher supply, address localized teacher shortages, and diversify the profession. However, little is known about the scale and design of GYO programs, which recruit and support individuals from the local community to become teachers. We conduct a quantitative content analysis to describe 94 GYO initiatives. We find that GYO is used broadly as an umbrella term to describe teacher pipeline programs with very different purposes, participants, and program features. Our results suggest that misalignment between some GYOs’ purposes and program features may inhibit their effectiveness. Finally, we propose a new typology to facilitate more precise discussions of GYO programs.
This study examines the experience of demotion from a principalship to an assistant principalship and how race and gender can differentially impact career trajectories. Using administrative state dataset of 10,946 observations at the principal level, we used probit regression to determine the overall probability of demotion and Kaplan Meier survival analysis to estimate the differences in probability over time. Our analysis describes not only who experiences demotions, but includes the characteristics of the sending and receiving schools. Survival analysis illustrates how small differences over time in demotion by race resulted in statistically significant systemic differences. We also find that experience matters: for every additional year of experience in the principal role, the probability of experiencing demotion decreases by 0.34%.
Career and Technical Education (CTE) prepares students for life beyond high school by providing practical labor skills, workforce credentials, and early post-secondary credits. States are required to report the number of CTE concentrators to receive federal Perkins funding, but systems of identifying students as concentrators vary among states. We analyzed two distinct concentrator identification strategies, one based on local education agency administrator reporting and another universal screening system using transcript data. Analyses revealed moderate amounts of mismeasurement in concentration status and modest amounts of systematic mismeasurement penalizing students who qualify for free or reduced-price lunch, English language services, and special education services.
This paper presents the results from a randomized controlled trial of Chapter One, an early elementary reading tutoring program that embeds part-time tutors into the classroom to provide short bursts of 1:1 instruction. Eligible kindergarten students were randomly assigned to receive supplementary tutoring during the 2021-22 school year (N=818). The study occurred in a large Southeastern district serving predominantly Black and Hispanic students. Students assigned to the program were over two times more likely to reach the program’s target reading level by the end of kindergarten (70% vs. 32%). The results were largely homogenous across student populations and extended to district-administered assessments. These findings provide promising evidence of an affordable and sustainable approach for delivering personalized reading tutoring at scale.
Providing ample opportunities for students to express their thinking is pivotal to their learning of mathematical concepts. We introduce the Talk Meter, which provides in-the-moment automated feedback on student-teacher talk ratios. We conduct a randomized controlled trial on a virtual math tutoring platform (n=742 tutors) to evaluate the effectiveness of the Talk Meter at increasing student talk. In one treatment arm, we show the Talk Meter only to the tutor, while in the other arm we show it to both the student and the tutor. We find that the Talk Meter increases student talk ratios in both treatment conditions by 13-14%; this trend is driven by the tutor talking less in the tutor-facing condition, whereas in the student-facing condition it is driven by the student expressing significantly more mathematical thinking. Through interviews with tutors, we find the student-facing Talk Meter was more motivating to students, especially those with introverted personalities, and was effective at encouraging joint effort towards balanced talk time. These results demonstrate the promise of in-the-moment joint talk time feedback to both teachers and students as a low cost, engaging, and scalable way to increase students' mathematical reasoning.
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -- one predicting course completion, the second predicting degree completion. Our results show that algorithmic bias in both models could result in at-risk Black students receiving fewer success resources than White students at comparatively lower-risk of failure. We also find the magnitude of algorithmic bias to vary within the distribution of predicted success. With the degree completion model, the amount of bias is nearly four times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores. Between the two models, the magnitude and pattern of bias and the efficacy of basic bias mitigation strategies differ meaningfully, emphasizing the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being less useful at predicting Black student success compared with White student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.
Many dimensions of teacher working conditions influence both teacher and student outcomes; yet, analyses of schools’ overall working conditions are challenged by high correlations among the dimensions. Our study overcame this challenge by applying latent profile analysis of Virginia teachers’ perceptions of school leadership, instructional agency, professional growth opportunities, rigorous instruction, managing student behavior, family engagement, physical environment, and safety. We identified four classes of schools: Supportive (61%), Unsupportive (7%), Unstructured (22%), and Structured (11%). The patterns of these classes suggest schools may face tradeoffs between factors such as more teacher autonomy for less instructional rigor or discipline. Teacher satisfaction and their stated retention intentions were correlated with their school’s working conditions classes, and school contextual factors predicted class membership. By identifying formerly unseen profiles of teacher working conditions and considering the implications of being a teacher in each, decisionmakers can provide schools with targeted supports and investments.