Methods Matter: Improving Causal Inference in Educational and Social Science Research
... To characterize patterns of project activity around the introduction of the documents, we adopt a modeling strategy derived from Regression Discontinuity (RD) methods [42]. 15 We identified the optimal bandwidth of 10 weeks for our time series analysis through using the Imbens-Kalyalaraman test through the rdd package [43]. ...
... However, the assumptions necessary to support causal inference in an RD framework are not satisfied in our setting, where the timing of document introduction could have many causes related to the underlying trend in contribution activity. See[42] for more on RD methods.16 https://cran.r-project.org/web/packages/rdd/rdd.pdf ...
README and CONTRIBUTING files can serve as the first point of contact for potential contributors to free/libre and open source software (FLOSS) projects. Prominent open source software organizations such as Mozilla, GitHub, and the Linux Foundation advocate that projects provide community-focused and process-oriented documentation early to foster recruitment and activity. In this paper we investigate the introduction of these documents in FLOSS projects, including whether early documentation conforms to these recommendations or explains subsequent activity. We use a novel dataset of FLOSS projects packaged by the Debian GNU/Linux distribution and conduct a quantitative analysis to examine README (n=4226) and CONTRIBUTING (n=714) files when they are first published into projects' repositories. We find that projects create minimal READMEs proactively, but often publish CONTRIBUTING files following an influx of contributions. The initial versions of these files rarely focus on community development, instead containing descriptions of project procedure for library usage or code contribution. The findings suggest that FLOSS projects do not create documentation with community-building in mind, but rather favor brevity and standardized instructions.
... Randomization does not need to occur at the individual level for causal inference. Designs where the unit of analysis is at an aggregate unit such as classroom, university, or school district are commonly applied to overcome the potential for the treated and untreated units to interact with each other, which contaminates the treatment-control contrast (Murnane & Willett, 2010). This contrast is alternatively referred to as the "stable unit treatment value assumption," which is necessary for propensity score methods to be implemented (Guo & Fraser, 2014;Hernan & Robins, 2020). ...
... Because this assumption is so critical to successful randomized experiments, it is often a reason these techniques are difficult to implement in practice. But even in instances where randomized experiments have issues, the use of instrumental variables (discussed below) can recover the causal effects (Murnane & Willett, 2010). ...
The purpose of this review is to advance the application of causal inference strategies to service-learning and community engagement and offer recommendations for both practitioners and researchers. The review offers an introduction to various techniques for yielding causal conclusions and discusses an example of the technique from the literature. The conclusion offers recommendations for pursuing causal inference and improving research designs related to service-learning and community engagement.
... Because COVID-19 and school closures were beyond the control of any researcher, practitioner, or child, the global pandemic represents a naturally occurring opportunity to examine this unique experience on children's developmental outcomes. To do so, we employed a regression discontinuity (RD) design -a quasi-experimental design that estimates the impact of intervention or exogenous "shock" by comparing the scores just before the event with those just following the event (Murnane & Willett, 2010). In this design, each child serves as his or her control, eliminating the need for a counterfactual. ...
Following a significant natural event (e.g., SARS-CoV-2, Hurricane Katrina), some young children adapted effectively while others face academic and social-emotional challenges (Goenjian et al., American Journal of Psychiatry 162(12) (2005)); (Joo & Lee, Child Indicators Research, 15 (2022)); (Stark et al., Psychological Trauma: Theory Research Practice and Policy, 12(S1) 2020); children from groups that are historically and institutionally marginalized are at greater risk of experiencing negative outcomes than their majority peers. This descriptive study addressed three primary questions: (1) Did the effects of COVID-19 disruptions on pre-kindergartener’s academic, social, and behavioral skills differ by racial/ethnic status?; (2) Were student-teacher contact and learning supports/barriers associated with young children’s academic, social, and behavioral skills during COVID-19 disruptions? Did these associations differ by racial/ethnic status?; and (3) Was there evidence of academic, social, and/or behavioral “recovery” among study participants during their kindergarten year? Data were collected from 108 pre-kindergarten children’s teachers via online surveys. Teachers reported no contact with 11% and frequent contact with 37.4% of students after in-person classroom instruction ceased. Common types of contact were in-person video-chats and pre-recorded lessons. Children from groups that are historically and institutionally marginalized more frequently had no contact with their teachers and fewer in-person video-chats and pre-recorded lessons. Teachers also reported access to learning materials, technology, and parent engagement/support was lower for children from historically and institutionally marginalized groups. A second wave of data collection revealed children had significant improvements in language and social skills from pre-kindergarten to elementary and small declines in behavior problems. Findings offer evidence that children from groups that are historically and institutionally marginalized received fewer learning supports immediately following COVID-19 disruptions but that primary-school teachers made a concerted effort to engage all children in positive learning experiences and frequent contact, serving as a protective factor against the potential negative impact that COVID-19 disruptions had on children’s learning and development.
... In our results, we report intent-to-treat (ITT) effects estimated with and without baseline covariate controls. 9 The primary purpose of including baseline controls is to improve the precision of our estimates (Lin, 2013;Murnane & Willett, 2010). ...
... Second, confounding votes may be viewed as an unobserved characteristic. The theory of regression discontinuity says that if observed covariates are balanced as if from randomization around the cutoff, unobserved covariates should be, too (Dunning, 2012;Murnane & Willett, 2010). Any such confounding factor would have to have different effects on cities that vote for and against new taxes, it would have to exist in periods after the vote but not before, and have different effects on high-and low-income cities. ...
We present the first capitalization study to look at crime fighting and house prices using the causal inference technique of regression discontinuity. It is also the first study on the link between police spending and housing sales volume. Voting to increase police taxes and spending by 15% is not linked to house prices or transaction volume overall. However, increasing spending causes 13% higher housing prices in low-income cities and at least a 14% decrease in house prices in high-income cities. The effects persist through all five years after the vote that we study. The sale price results are most consistent with overfunded police departments, rather than Tiebout sorting, neighborhood instability, or signaling. Our results suggest that the small or non-existent link between house prices and crime found by the literature really just reflects the sum of large but opposite moves in house prices in different market segments.
... Educational interventions play a significant role in the improvement of student outcomes by providing evidence for the effectiveness of current educational instruction and strategies [6]. Typically, the target of a statistical analysis of an educational intervention is the Average Treatment Effect (ATE). ...
... Families experiencing extreme changes might have unique characteristics that influence how SES affects their children's education (Duncan et al., 2011). This limitation affects the generalizability of findings to the broader population (Murnane & Willett, 2010). Second, the influence of SES on educational outcomes may be subject to critical thresholds or tipping points. ...
A growing body of research has examined the relationship between family socioeconomic status (SES) and educational outcomes. Meta-analyses of raw correlations generally indicate moderate associations, typically between 0.12 and 0.3 for academic achievement and around 0.18 to 0.4 for educational attainment. Causal inference studies, aimed at capturing the true effects of SES, report much weaker associations, usually around 0.1 or less. Despite the importance of these causal estimates, few studies have systematically reviewed evidence from causal research. To address this gap, we conducted a systematic review of studies on the causal effect of SES on educational achievement and attainment. A total of 24 causal inference studies published between 1990 and 2023 were reviewed. The findings contribute to the literature and theory in several ways. First, the meta-analysis revealed a small and non-significant effect of SES on academic achievement (Cohen’s d = 0.03) and a small but statistically significant effect on educational attainment (d = 0.08). Second, moderator analyses indicated that parental education exerted a stronger influence on educational attainment than that of family income. Moreover, the absence of significant differences in SES effects between developed and developing countries, as well as across various causal inference research designs (i.e., sample size, model specification, and methodologies), calls into question the assumed context-dependent nature of SES influence. Overall, the findings challenge SES-centered theories, showing that the causal impact of family SES on educational outcomes is much smaller than generally believed, and suggest that universal mechanisms may underlie the SES-education relationship.
... This concept is different from compliance, because the control units are not receiving the treatment directly from the providers of the treatment but rather indirectly from others who took up the treatment formally. Spillover effects are an internal validity problem because they reduce the treatment contrast between treated and control units within an RCT and attenuate the treatment effect estimates (Murnane & Willett, 2011). Monitoring efforts that measure either the interaction between treated and control units or directly assess whether the control students are receiving the treatment can mitigate this SUTVA concern. ...
The objective of this article is to discuss the advantages of effective educational monitoring in the context of a longitudinal RCT. Intentional data collection and monitoring enables the important assessment of issues of both internal and external validity. We discuss how we used mixed methods data collection to reveal important changing contextual factors in an evaluation of a postsecondary access program in the U.S. state of Texas. Specifically, we employed quantitative analysis of the RCT to compare the college enrollment rates of high schools that were randomly assigned a college adviser with schools that were not assigned a college adviser. We employed survey data collection, qualitative interviews, and site visits to monitor the fidelity of treatment implementation and compliance to treatment assignment over time. In the absence of monitoring treatment fidelity and compliance over time in both treatment and control schools, we would have missed critical changes that explain the observed attenuation of treatment effect estimates. We also discuss how monitoring can inform defenses of the stable unit treatment value assumption and suggest how effective the program will be when applied more widely or to other contexts.
... Causal models allow for the assessment of the potential impacts of different interventions, aiding in informed decision-making. In the field of education, causality helps in determining education interventions and reforms, implementing personalized learning approaches, and assessing teaching methods [144]. It aids in understanding attrition factors and enables predictive analytics for educational outcomes. ...
Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control , sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution , and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.
... Causal Discovery can be useful in various research fields such as social science [1] [2] or biology [3]. It aims to find causal relations between random variables of a system. ...
Causal Discovery aims to reveal the causal structure in a system of random variables. It can be used in many fields such as social science or biology to find causal relations between variables. Traditional Causal Discovery methods often use only observational data, which can limit their abilities to find relations. In addition, they are often extremely resource-hungry and inefficient. To address this problem, recent scientific works try to use Reinforcement Learning (RL) in combination with Deep Learning (DL) to actively intervene on variables of a system to gather information about its structure. This paper gives an introduction on how to use Deep Reinforcement Learning to gather the causal structure of a system. I explain Deep Reinforcement Learning and Causal Discovery as well as interventions. After that, I dive into some recent Causal Discovery methods using DRL. The paper concludes by giving an outlook on the most important challenges that are still to be solved.
... However, this assumption is often violated when units are interconnected with other units through social or physical interactions. For example, in education, students participating in tutoring programs may exert an influence on the academic performance of their classmates through information sharing and peer interactions (Murnane and Willett, 2010). Similarly, behavioral interventions such as training sessions designed to reduce health risk behavior (e.g., unprotected sex, alcohol and drug use, smoking), may have an effect on individuals beyond those receiving the intervention (Buchanan et al., 2018). ...
Behavioral health interventions, such as trainings or incentives, are implemented in settings where individuals are interconnected, and the intervention assigned to some individuals may also affect others within their network. Evaluating such interventions requires assessing both the effect of the intervention on those who receive it and the spillover effect on those connected to the treated individuals. With behavioral interventions, spillover effects can be heterogeneous in that certain individuals, due to their social connectedness and individual characteristics, are more likely to respond to the intervention and influence their peers' behaviors. Targeting these individuals can enhance the effectiveness of interventions in the population. In this paper, we focus on an Egocentric Network-based Randomized Trial (ENRT) design, wherein a set of index participants is recruited from the population and randomly assigned to the treatment group, while concurrently collecting outcome data on their nominated network members, who remina untreated. In such design, spillover effects on network members may vary depending on the characteristics of the index participant. Here, we develop a testing method, the Multiple Comparison with Best (MCB), to identify subgroups of index participants whose treatment exhibits the largest spillover effect on their network members. Power and sample size calculations are then provided to design ENRTs that can detect key influencers. The proposed methods are demonstrated in a study on network-based peer HIV prevention education program, providing insights into strategies for selecting peer educators in peer education interventions.
... Drawing a causal inference from a difference-in-differences estimate relies on the assumption that the treatment group, absent the intervention, would have continued on a parallel trajectory to the comparison group in the post-treatment period. This assumption is not technically directly testable as we cannot observe what would have occurred absent the intervention but we can assess evidence in support of this assumption by examining whether the treatment and comparison groups were on a similar trajectory on the outcome in the years leading up to the intervention (Angrist and Pischke 2009;Murnane and Willett 2010). Event study methods allow for an assessment of whether or not this was true. ...
Does centralization affect the performance of public institutions in the long run? Can states more successfully improve struggling bureaucracies than local governments? This paper explores these questions in the context of educational governance with a focus on state takeovers of school districts in the U.S.—a shift away from the traditional school board governance arrangement toward more centralized decision-making at the state level. In recent decades, takeovers have become a more common policy response to perceived low performance of public school systems. This paper extends an earlier study on the topic to examine the longer-run effects of this form of political centralization on system performance. Using a nationwide sample and up-to-date event study methods, the paper finds no evidence that takeovers of districts between 2010 and 2018 generated improvements in student reading and math performance, up to nearly a decade after takeover occurred. Takeovers lasting a greater number of years are not associated with differential impacts. Findings are not driven by compositional changes in student populations or bias due to variation in treatment timing. This form of political centralization from local to state levels therefore does not appear to be a consistent tool for improving the performance of public institutions.
... This study employs quasi-experimental research design to examine the relationship between state policies, school practices, and student outcomes across the 50 states. Specifically, this study employs the difference-in-differences (DID) method for comparing pre-pandemic vs. post-pandemic achievement differences among 50 states (Murnane & Willett, 2010). Here, we focus on interstate variations in grades 4 and 8 reading and math achievement trends among the states, as differentiated by their remote learning policies and practices. ...
This study provides critical policy insights into the U.S. students’ academic achievement trends and the impacts of remote learning policies and practices during the COVID-19 pandemic. Linking cross-state education assessment and survey datasets, it applies multivariate regression and case study methods to examine the relationship between remote learning policies and student achievement in reading and math. The results reveal large cross-state variations in outcomes along with regional patterns of in-person vs. remote learning policy divides. The states that adopted top-down, stringent school closure/reopening policies and relied more on remote instruction experienced relatively larger achievement declines. The government’s funding support, teacher help, and home learning resources such as technology did not work. In contrast, the states that adopted flexible school closure/reopening policies with more in-person instruction reported smaller achievement losses. Further, students’ digital literacy and remote learning self-efficacy such as online searching and help-seeking worked. The policy and research implications are discussed.
... The constructs that were measured in the study include education, training programs, academic achievement, and earnings. The constructs of education and training were based on Aguinis and Kraiger (2009) as well as Murnane and Willett (2018). Contentrelated validity of each item was assessed by three specialists in education and labour economics. ...
In this study, researchers explored how education, training, and earning potential are connected among young professionals in Pakistan, focusing on the mediating role of academic achievement. Using data from 200 respondents in Lahore, the study employed a cross-sectional survey design and structural equation modelling to test the proposed relationships. The findings indicate that education and training significantly boost earning potential, with academic achievement playing a key mediating role. However, systemic challenges such as underfunded educational infrastructure and a mismatch between training programs and labour market needs hinder the full realisation of earning potential in Pakistan. These insights are valuable for policymakers, educators, and industry stakeholders, emphasising the need for strategic alignment between education, training, and market demands to improve labour market outcomes. The study also extends the human capital theory by contextualising it within the unique socio-economic environment of a developing country.
... A total of 244 students were assessed at baseline in September 2021, and 179 at endline in November/December 2021 (26% attrition rate). While the attrition rates are not ideal and reduce external validity (Murnane & Willett, 2011), they are not unusually high given the context of telephone assessment during the pandemic (Rodriguez-Segura & Schueler, 2022). In terms of internal validity, attrition rates were slightly higher in the treatment group (24% for control and 29% for treatment groups). ...
The use of SMS messaging for education has grown in recent years, with particular attention recently during the Covid‐19 pandemic. Mobile phones often have high levels of ownership in low‐income contexts compared to computers, and lower connectivity requirements, which arguably make this a more equitable medium than data‐heavy online instruction, for example. However, given that gender can be a factor to influence mobile device access and use, it is also important to consider educational applications through a gender lens, to avoid further exacerbating digital divides. In this paper, we present an analysis of server log and evaluation data in relation to a literacy‐focused initiative for primary‐aged learners carried out in Kenya as part of the Tusome programme and through the SMS‐based M‐Shule education platform, which does not require an Internet connection or smartphone to run. The extent of engagement with the platform varies according to gender and location within the country. The data also demonstrate a positive impact on learning outcomes regardless of learners' gender and location. Furthermore, the learning gains are shown to be relatively cost‐effective in comparison with educational technology interventions in similar contexts. The findings show that this low‐connectivity adaptive model has a positive impact on learning outcomes. It is a scalable approach to support a range of learners in Kenya, providing more support to learners who need it, and leading to increased foundational learning outcomes overall. As such, the findings will also be of highly relevant to other low‐connectivity contexts.
Practitioner notes
What is already known about this topic Mobile phones can be used as a means to support learning, through mobile learning and SMS, particularly in low‐connectivity contexts, although there is a lack of rigorous evidence of impact upon learning outcomes.
Mobile phone device ownership tends to be higher than computer or wired Internet connections in many low‐income contexts.
Software applications which adapt to the learners' level have shown good potential for gender‐equitable learning outcomes in low‐income contexts; however, these often require an Internet connection in addition to computers or tablets to be run on.
What this paper adds There is a lack of contextually relevant evidence of the impact of SMS‐based mobile learning applications in low‐resource and low‐connectivity contexts upon learning outcomes.
Through analysis of data generated via an experimental design, this study provides evidence that literacy materials delivered through an SMS‐based educational platform—M‐Shule—can have a positive impact upon learning outcomes.
Furthermore, gains are equitable in terms of learners' gender, and location, within Kenya.
Implications for practice and/or policy Mobile phones can be an effective way of reaching learners to provide additional educational support as part of existing education programmes in low‐connectivity environments.
Learning gains using M‐Shule are evidenced as significant and relatively cost‐effective.
Existing high‐quality learning materials developed in other media can be effectively adapted to SMS to reach learners particularly who are out‐of‐school or during periods of educational disruption.
... Such research should be conducted as randomized control trials or field experiments (Harrison & List, 2004;Maris et al., 2024;Nisa et al., 2019). These trials require measurement of appropriate outcome variables that align with program goals, random assignment into control and treatment groups, and appropriate statistical testing (Murnane & Willett, 2010). Testing with a range of different groups could be helpful to understand the extent to which the findings from a national-level, quantitative study such as this can be applied to individual groups working towards specific ends within specific communities (Prysby, 2020). ...
Urban landscapes play a crucial role in the health of freshwater ecosystems. The task of protecting and restoring urban freshwater waterways requires concerted efforts from all sectors of society, including volunteers. The recruitment and retention of volunteers is often a challenge for community environmental organizations as urban residents are diverse and influenced by a blend of personal, societal, and environmental factors. We surveyed a representative sample of 1901 urban residents across Aotearoa New Zealand and used the Behaviour Change Wheel framework and audience segmentation to understand the underlying factors influencing volunteering for waterway restoration projects and to identify potential target audiences to recruit new volunteers. We identified four segments within the target audience (“Supportive,” “Receptive but unsure,” “Hesitant and lack opportunity,” and “Reluctant”) each with its own unique profile of capabilities, opportunities, and motivations for volunteering. Recommendations for appropriate intervention designs to increase levels of volunteering include providing tailored messaging and events for those who are “Receptive but unsure” or “Hesitant and lack opportunity” and information about volunteering opportunities to “Supportive” individuals. This knowledge lays the groundwork for future initiatives focused on increasing urbanites' volunteering with community freshwater restoration groups.
... Studies (Danzig, 2009;Darling-Hammond et al., 2009;Donaldson, 2008;Fullan, 2005;Murnane & Willett, 2010) demonstrate that when adult development is supported well, schools become places where everyone can progress. Research has demonstrated that when people learn and develop in schools, pupils gain, and their achievement increases (Donaldson, 2008;Guskey, 1999;Mizell, 2007). ...
In higher education, successful organizational performance, innovation, and institutional culture molding all depend on effective leadership. This review of the literature delves into the complex aspects of leadership in the context of higher education, looking at how it affects professors, faculty, staff, students, and organizational results. The review explores the changing role of leaders in fostering diversity, inclusivity, and academic performance by synthesizing recent findings. It also tackles the particular difficulties faced by academic leaders, like negotiating faculty governance, handling financial restraints, and adjusting to rapidly evolving technology environments. This review intends to offer insightful information to administrators and scholars who are looking to improve leadership effectiveness and meet the changing demands of contemporary universities. It does this by analyzing these opportunities and difficulties.
... We used PSM to identify a comparison group from those who were not in the BUILD PODER program. PSM is a procedure that can control for selection bias, the unobserved difference between those who chose to participate in the program and those who chose not to (Murnane & Willett, 2010). The primary objective of PSM is to balance confounding factors between the treatment and control groups. ...
The current study examined how participation in an undergraduate research experience (URE) that provides a counterspace affects sociocognitive factors (science self-efficacy, science identity, and academic self-concept) and how they, in turn, may shape science career intention in a sample of STEM undergraduate students. STEM majors from a public university in California completed surveys from 2017 to 2020 and rated their science identity, science self-efficacy, academic self-concept, and their intention to pursue a science-related research career. Structural equation modeling shows that URE participants reported higher (a) science self-efficacy, (b) science identity, and (c) academic self-concept relative to students who did not participate in the URE. While there was an indirect effect of science self-efficacy on science career intention mediated by science identity, higher science self-efficacy and academic self-concept were negatively associated with the intention to pursue a science career. MANOVA results suggest that URE participants fared better than non-URE students in all outcomes across all sub-groups. These results highlight the importance of an identity-focused UREs and the counterspace it fosters among STEM majors from diverse backgrounds.
... La educación primaria, que abarca los primeros años de la educación formal, es fundamental para el desarrollo cognitivo y social de los estudiantes. Durante este período, los estudiantes adquieren conocimientos fundamentales en lectura, escritura y aritmética, al mismo tiempo que desarrollan habilidades de pensamiento crítico y resolución de problemas que serán esenciales en su futuro académico y profesional (Murnane & Willett, 2011). La introducción de programas STEM en este nivel educativo no solo refuerza la instrucción de las disciplinas científicas y matemáticas, sino que también promueve la competencia tecnológica y la creatividad, aspectos que están adquiriendo una relevancia creciente en el ámbito laboral actual. ...
La incorporación de la educación STEM (Ciencia, Tecnología, Ingeniería y Matemáticas) en la enseñanza primaria se ha resaltado como una táctica esencial para formar a los estudiantes ante un futuro caracterizado por progresos tecnológicos y una economía fundamentada en el saber. El objetivo de este estudio es investigar las estrategias pedagógicas empleadas en la implementación de programas STEM en la educación general básica, evaluando su influencia en el desempeño académico y en el desarrollo de habilidades críticas como el pensamiento lógico, la creatividad y la resolución de problemas. El estudio utiliza una metodología mixta que integra técnicas cuantitativas y cualitativas con el fin de obtener una perspectiva completa sobre la influencia de la educación STEM. Se llevaron a cabo análisis estadísticos con el fin de evaluar el desempeño académico de los estudiantes en las áreas de ciencias y matemáticas, tanto previo como posterior a la introducción de programas STEM. Por otro lado, se realizaron entrevistas y encuestas a docentes y estudiantes con el propósito de obtener una comprensión más detallada de las percepciones y experiencias vinculadas a dichos programas. Según los resultados cuantitativos, los estudiantes que se involucraron en programas STEM presentaron mejoras significativas en su desempeño académico en contraste con aquellos que no participaron en dichos programas. Los resultados cualitativos indicaron que los estudiantes demostraron un incremento en su interés y motivación por las disciplinas científicas y tecnológicas. Esto sugiere que la educación STEM también influye de manera positiva en la disposición hacia el proceso de aprendizaje. No obstante, el estudio señala diversos obstáculos que inciden en la ejecución exitosa de la enseñanza STEM en la educación primaria. Uno de los desafíos identificados es la carencia de capacitación especializada para los profesores, quienes frecuentemente carecen de las competencias y conocimientos requeridos para impartir de forma integrada las materias STEM. Asimismo, se destaca la insuficiencia de recursos tecnológicos y materiales didácticos apropiados, particularmente en instituciones educativas que sirven a comunidades desfavorecidas. Las limitaciones mencionadas resaltan la importancia de adoptar un enfoque integral que contemple la capacitación constante del personal docente, la implementación de un currículo flexible que favorezca la integración de las disciplinas STEM y un aumento significativo en la asignación de recursos educativos. En resumen, la educación en las áreas de Ciencia, Tecnología, Ingeniería y Matemáticas (STEM) posee un potencial considerable para elevar el desempeño académico y preparar a los estudiantes para los retos venideros. No obstante, para llevar a cabo su aplicación de manera efectiva, es imperativo vencer obstáculos estructurales y brindar el respaldo adecuado tanto a los educadores como a los educandos.
... Causal inference aims to estimate the causal effect of a treatment on an outcome, adjusting for confounding factors that may influence both the treatment assignment and the outcome (Bembom & van der Laan, 2007;Crown, 2019;Cui et al., 2020;Grimmer, 2014;Kuang et al., 2020;Murnane & Willett, 2010;Ohlsson & Kendler, 2020;Sobel, 2000;Yao et al., 2021). In the context of continuous treatments, the estimation of causal effects becomes more complex than in the binary treatment case due to the need to model the treatment effect as a continuous function rather than a fixed difference (Hirano & Imbens, 2004;Lee, 2018;Rothenhäusler & Yu, 2019;Wu et al., 2022). ...
This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables. Across three distinct sets of Data Generating Processes (DGPs), the ps-BART model consistently outperforms the BCF model, particularly in highly nonlinear settings. The ps-BART model's robustness in uncertainty estimation and accuracy in both point-wise and probabilistic estimation demonstrate its utility for real-world applications. This research fills a crucial gap in causal inference literature, providing a tool better suited for nonlinear treatment-outcome relationships and opening avenues for further exploration in the domain of continuous treatment effect estimation.
... The quest to ascertain causal relationships lies at the heart of empirical research, transcending disciplines to unravel the mechanisms by which interventions alter outcomes (Bembom & van der Laan, 2007;Crown, 2019;Cui et al., 2020;Grimmer, 2014;Kuang et al., 2020;Murnane & Willett, 2010;Ohlsson & Kendler, 2020;Sobel, 2000;Yao et al., 2021a). It is crucial, though, to distinguish between correlation and causation. ...
This research aims to propose and evaluate a novel model named K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART) for improved estimation of Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). The study employs synthetic and semi-synthetic datasets, including the widely recognized Infant Health and Development Program (IHDP) benchmark dataset, to validate the model's performance. Despite promising results in synthetic scenarios, the IHDP dataset reveals that the proposed model is not state-of-the-art for ATE and CATE estimation. Nonetheless, the research provides several novel insights: 1. The ps-BART model is likely the preferred choice for CATE and ATE estimation due to better generalization compared to the other benchmark models - including the Bayesian Causal Forest (BCF) model, which is considered by many the current best model for CATE estimation, 2. The BCF model's performance deteriorates significantly with increasing treatment effect heterogeneity, while the ps-BART model remains robust, 3. Models tend to be overconfident in CATE uncertainty quantification when treatment effect heterogeneity is low, 4. A second K-Fold method is unnecessary for avoiding overfitting in CATE estimation, as it adds computational costs without improving performance, 5. Detailed analysis reveals the importance of understanding dataset characteristics and using nuanced evaluation methods, 6. The conclusion of Curth et al. (2021) that indirect strategies for CATE estimation are superior for the IHDP dataset is contradicted by the results of this research. These findings challenge existing assumptions and suggest directions for future research to enhance causal inference methodologies.
... Second, it minimizes sample deletion, thereby maximizing the information contained in the full sample. Third, it reduces the large number of covariates in a multiple regression model to a single propensity score, resulting in a more concise presentation of the results [34]. ...
Background: The roles of both parents’ and children’s educational expectations in shaping adolescent depressive symptoms have increasingly been discussed, yet in a separate manner. To date, few studies have associated parent–child mismatch in educational expectations with depressive symptoms, and less is known about the variation in the association across gender (male vs. female), educational level (primary vs. secondary), and region status (urban vs. rural) in the Chinese educational setting. Methods: Respondents were from a nationally representative sample of adolescent students in China (sample size: 1844; age range: 10–15 years). Parent–child mismatch in educational expectations included three categories: (1) “match”, (2) “mismatch—parent higher”, and (3) “mismatch—parent lower”. Regression analysis with inverse propensity-score weighting was employed to estimate the effect of parent–child mismatch as to educational expectations on depressive symptoms, and stratified analysis was used to examine the variation of the effect by gender, educational level, and region. Results: Compared with the “match” group, the “mismatch—parent higher” group had significantly higher levels of depressive symptoms. Furthermore, the pattern remained consistent between boys and girls, but differed significantly by adolescents’ educational level and region status. Specifically, the pattern was more pronounced in the primary school and urban subsamples. Conclusions: Findings in this study indicated that educators and policymakers can develop tailored strategies to alleviate depressive symptoms among the “mismatch—parent higher” group, and especially for those children from primary schools and urban areas.
... In post-test results, participants either perceived themselves worse or there were no significant differences for curiosity and adaptability, compared with self-perceptions of students from the comparison group. An explanatory hypothesis may be the "John Henry" effect, as mentioned in the literature, in which the comparison group seeks to compensate for not being part of the project with an extra effort to develop these skills (Murnane and Willett, 2010). ...
Universal school-based socio-emotional learning (SEL) programs for adolescents have shown their efficacy in producing positive outcomes. The aim of the current study is to present an original school-based program and project for adolescents—Semear Valores On-air – and to assess the relationship between participation in the project and students’ socio-emotional skills. Based on the character strengths and virtues model, this online school radio project aimed at promoting communication, creative thinking, adaptability, and resilience skills in adolescents and giving them the opportunity to become influential agents of well-being and citizenship. As part of the school curriculum, students were invited to create and record radio shows and podcasts. An online school radio was thus created, and it continues to broadcast all over the world, with music, daily shows, and interviews 24/7. It was developed within the framework of the Gulbenkian Academies for Knowledge, a nationwide Portuguese program, that seeks to prepare children and youth for change, to enable them to deal with complex problems, and to expand their opportunities for achievement. A quasi-experimental design, with a mixed qualitative-quantitative approach was used to analyze data collected from 112 adolescents in the second year of its implementation, in 2020–2021. Results suggest that (1) teachers’ perceptions of student’s socio-emotional skills in the post test showed more positive associations with the participation in the project, than participant’s perceptions; (2) students identified eight types of lessons learned, the one most referred was the improvement of socio-emotional skills and learning about themselves; and (3) the combined opportunities for adolescents to learn more about themselves, to express themselves and to practice socio-emotional skills are important ingredients for their motivation and active engagement in the project. Overall, these results indicate that participation in the project is associated with positive outcomes for the adolescents and that both monitoring and evaluation data are very important to interpret the outcomes in a more comprehensive manner.
... It is all too often the case that scientifically credible evidence about the effectiveness of a particular intervention has not been collected. The effectiveness of behaviour change interventions should be rigorously evaluated against program goals using scientifically robust methods such as treatment and control groups, random assignment, and the use of appropriate statistical tests to determine whether the intervention made a difference and worked as intended (Murnane and Willett 2010). ...
Urbanisation poses numerous challenges to fresh water biodiversity, and installation of above-ground rainwater tanks on private properties is one important tool for addressing these challenges. In this paper, we used an online survey targeting a representative sample of 1291 urban residents to investigate their underlying capabilities, opportunities, and motivations for installation of above-ground rainwater tanks on their properties. Only 221 (17%) of the 1,291 respondents already had an above-ground rainwater tank installed on their property. By segmenting on behavioural variables, we found that those urban residents who currently did not have a water tank on their property were not homogeneous. We identified three audience segments, 'Supportive' (19%), 'Receptive' (59%) and 'Reluctant' (22%), each with their own unique driver and barrier combination to rainwater tank installation. Suitable leverage points and behaviour change tools (e.g. education, enablement, incentivisation) and message framing (environ-mental, community, or functional benefits) are identified for each segment. ARTICLE HISTORY
... This strategy accordingly requires revisiting and revising pre-registration plans before analysis. Just as empirical education researchers have benefited from other best practice guides (e.g., Bloom, 2012;Calonico et al., 2017;Duflo et al., 2007;Imbens & Lemieux, 2008;Lipsey et al., 2015;Murnane & Willett, 2010), we hope our present work might do the same or at least spark further work on this topic. There is still much that can be learned from studies that were compromised by the COVID-19 crisis. ...
... In the absence of an experiment that could determine CareerAdvance participation, we addressed the concern of parents' nonrandom selection into the program-which would yield biased estimates of treatment effects-with propensity score matching, estimating the likelihood of a parent-child dyad enrolling in the CareerAdvance two-generation intervention rather than Head Start alone. Although matching strategies do not account for differences between the treatment and comparison groups along unobservable determinants of program participation (Murnane & Willett, 2010;Rubin, 2001), propensity score matching allowed us to compare CareerAdvance children to other CAP Tulsa Head Start children with similar observable characteristics. We recruited CAP Tulsa families (i.e., parent-child dyads) into the matched comparison group based on an earlier stage of propensity score matching that drew on CAP Tulsa's administrative data. ...
This study explores the effects of the two‐generation program Career Advance —which combines education and training for parents in healthcare with Head Start for children—on children's academic, language, mathematics, and inhibitory control followed for 3 years. The sample (collected in Tulsa, Oklahoma from 2011 to 2018) includes 147 children in the Career Advance group and 139 children in a matched comparison group ( n = 286; 40% Black, 17%, White, 10% Hispanic, 33% Mixed Race, or Other Race; M = 3.6 years old; 47% female). Overall, the effect of Career Advance on child outcomes is neither greater nor less than Head Start alone. These findings suggest that children's developmental outcomes do not worsen or improve in the short term when their parents return to school.
... For example, parents who encouraged their children to participate in the intervention program may care deeply about the quality of their children's education. These same parents may also have been more likely to try to enhance their children's skills at home by emphasizing the importance of reading or by checking their children's homework (Murnane & Willett, 2010). In this study, we used four matching estimators (i.e., formal training or workshop, conducting own research, honors and awards, and conference attendance or presentation) to control for selection bias while producing the PSM scores using SPSS 25.0 (IBM, 2017). ...
In 2014, the NIH Diversity Program Consortium (DPC) launched an initiative to implement and evaluate novel interventions at a variety of academic institutions across the country to engage undergraduate students from diverse backgrounds in biomedically-related research. The local intervention examined in the current study provides Critical Race Theory (CRT)-informed mentoring, more broadly called critical mentoring, for its participants. We examined the relationship between critical mentoring and student outcomes. In this study, student outcomes consisted of three components: (a) mentor satisfaction, (b) science identity, and (c) science self-efficacy. To determine student outcomes, we used the 2020 Student Annual Follow-up Survey (SAFS). We found that participants in the intervention program reported higher levels of critical mentoring than non-intervention participants and critical mentoring was, in turn, predictive of higher. mentorship satisfaction, science identity , and science self-efficacy. This finding implies that the CRT-informed intervention was more effective by developing an environment in which high-quality, critical mentors influenced students' sense of science identity and self-efficacy. Additionally , we also found that intervention participants reported higher science identity and science self-efficacy than non-intervention participants, which suggests that the intervention cultivated science identity and self-efficacy in other ways outside of critical mentorship as well. The current study highlights how participation in an intervention program can increase science identity and self-efficacy, two factors pre-dictive of science career intentions. The connection between critical mentoring practices and increased science identity and self-efficacy underscores the significance of culturally and racially relevant social support in science education.
... Researchers have long observed that measurements become less reliable the more they are used for decision-making, especially when observations are close to a decision rule [9,27]. For example, education researchers wrestle with the reliability of measurements that are used for decisions such as school allocation, graduation, and college admissions [45,55]. Parallel scholarship has studied the problem of publication bias in science that result from creative efforts by scientists to navigate p-value thresholds [32]. ...
In July 2023, New York City became the first jurisdiction globally to mandate bias audits for commercial algorithmic systems, specifically for automated employment decisions systems (AEDTs) used in hiring and promotion. Local Law 144 (LL 144) requires AEDTs to be independently audited annually for race and gender bias, and the audit report must be publicly posted. Additionally, employers are obligated to post a transparency notice with the job listing. In this study, 155 student investigators recorded 391 employers' compliance with LL 144 and the user experience for prospective job applicants. Among these employers, 18 posted audit reports and 13 posted transparency notices. These rates could potentially be explained by a significant limitation in the accountability mechanisms enacted by LL 144. Since the law grants employers substantial discretion over whether their system is in scope of the law, a null result cannot be said to indicate non-compliance, a condition we call ``null compliance." Employer discretion may also explain our finding that nearly all audits reported an impact factor over 0.8, a rule of thumb often used in employment discrimination cases. We also find that the benefit of LL 144 to ordinary job seekers is limited due to shortcomings in accessibility and usability. Our findings offer important lessons for policy-makers as they consider regulating algorithmic systems, particularly the degree of discretion to grant to regulated parties and the limitations of relying on transparency and end-user accountability.
... In mathematics education research, accurately measuring student growth due to instruction is vital to answering fundamental questions about individual learning and education (Murnane & Willett, 2011). Consequently, math learning interventions with a pretest-posttest design are critical as tools that allow obtaining evidence supporting the effectiveness of teaching methodologies, educational devices, didactic materials, and other factors crucial to sustaining and fostering learning environments. ...
ABSTRACT
Traditional analyses of pretest-posttest designs draw inferences about
learning using linear models that predict posttest performance by account
ing for pretest scores. These analyses allow inferring student growth effects
but prior research has pointed out some conceptual problems, such as its
meaningful interpretation as learning. By drawing on research about phys
ics teaching, this study introduces the Learning Rate Model (LR-Model) to
describe learning as a non-linear process of knowledge acquisition.
Analysis of data gathered from two real-world math learning interventions
indicates that the model can provide adequate interpretations of math
learning phenomena observed in pretest-posttest study designs. The
model seems well suited to assess math learning interventions that teach
unfamiliar knowledge to learners, where the pretest and posttest measures
are designed to evaluate the content delivered during the intervention.
Under these conditions, the LR-Model shows advantages to alternative lin
ear models as (1) it describes learning more accurately, (2) it delivers more
meaningful learning estimators, and (3) it provides higher statistical power
for hypothesis testing aimed at determining learning effects.
... The normal distribution of the three measures analyzed allowed us to perform a parametric analysis. First, in preliminary analysis, we examined the normal distribution and differences based on sex and teacher-class, which were taken as covariates, as previous studies have indicated the relevance of these variables and the need for them to be controlled (Murnane & Willett 2011;Rodríguez et al., 2021a). ...
Cognitive Self-Regulation Instruction (CSRI) program is a strategy-focused instruction with three instructional components for improving students’ writing product (direct teaching, modelling, and peer-practice). The present study aimed to explore whether the CSRI program leads to spontaneous transfer, improving the writing product (in terms of quality, structure, and text coherence) of an uninstructed genre (opinion text); and to examine whether the order in which the instructional components were implemented had an effect. A total of 126 students in their 4thyear of primary school participated in the study. They were randomly assigned to one of two experimental conditions which received the CSRI but differed in the order the instructional components were delivered, or to a control condition which followed the traditional teaching approach. Our findings show that both CSRI sequences produced benefits in terms of greater structure and coherence of the writing product in the opinion text at post-test but not 8months after the intervention. In consequence, for students to be able to adequately transfer strategies to uninstructed text genres, they need teachers to teach them how to do it effectively.
El programa de Instrucción de la Autorregulación Cognitiva (CSRI) se basa en la instrucción centrada en la estrategia e incluye tres componentes para la mejora del producto textual (enseñanza directa, modelado y práctica entre iguales). Se plantearon como objetivosanalizar si la instrucción con el programa CSRI en un género textual (texto de comparación) conducía a la transferencia espontánea en el producto textual (mejor coherencia, estructura y calidad) en un género no instruido (texto de opinión); y examinar si el orden de los componentes instruccionales presentaba algún efecto. Participaron 126 estudiantes de cuarto de educación primaria que fueron asignados a una de las dos condiciones experimentales en las que se aplicaba el programa CSRI (con diferente secuencia de los componentes instructivos), o a una condición de control en la que se seguía la enseñanza tradicional. Los resultados reflejaron que las dos secuencias de instrucción del programa CSRI mostraban beneficios en la estructura y coherencia del producto textual de los estudiantes en el posttest pero no 8 meses después. Para que los alumnos sean capaces de transferir adecuadamente lo aprendido a géneros textuales no instruidos, necesitan que los profesores les enseñen cómo hacerlo eficazmente.
This study examines bullying victimisation and sense of belonging among immigrant students across European countries, using data from PISA 2022 and ESS 10. Applying a social-ecological multilevel framework, we account for student, school, and national factors. Findings indicate that recent immigrants experience higher levels of bullying and lower school belonging than native peers, even after controlling for socio-economic status and language spoken at home. Societal attitudes toward immigrants significantly predict these outcomes—more positive attitudes are associated with more favourable experiences. In contrast, educational migration integration policies do not explain additional variance. These results are concerning amid rising refugee populations in Europe, given the profound personal and societal consequences of bullying and low belonging, including increased rates of juvenile delinquency.
In the article, we use an incremental science approach to propose opportunities for researchers in educational psychology and special education to design and implement studies that are inclusive of theory development and students with disabilities. We describe the goals and purpose of incremental science in special education and how the incremental science framework is aligned with research methodologies and funding mechanisms. We provide examples of research that have advanced knowledge in special education and discuss the importance of individual differences and disability as diversity, intersectionality, and opportunities to advance and deepen the understanding of special education and students with disabilities’ experiences by expanding the methodological approaches used across the incremental frameworks, highlighting methods and procedures used in special education. We conclude by emphasizing the importance of studying phenomena in real-world, complex contexts and provide additional examples and avenues for researchers to use incremental science to address both individual differences and structural challenges in education.
Desirable working conditions such as geographic accessibilities attached to the job are important amenities for individuals to maximize their occupational utility, and the absence of comfortable working conditions should be equally and adequately compensated. The “compensating wage differential” hypothesis subsumed by hedonic wage theory offers us the theoretical framework to interpret the relationship between individuals’ earnings and job dis(amenities). In the teacher labor market, job disamenities such as the undesirable geographic location of schools should be compensated to balance the spatial distribution of teachers. The authors tested this idea by employing a unique self-collected dataset, sourced from a representative developing region in southern China. The results showed that teachers’ earnings were positively associated with the geographical unpopularity attached to the rural school where they worked, holding other factors constant. In addition, the result yielded by instrumental variable identification strategy confirmed that teachers’ positive self-selection into rural schools had led to an underestimation of the adverse impact of geographic ruralness on their utility losses. This paper sheds light on how to staff schools located in geographically disadvantaged areas with quality teachers by properly offering economic incentives.
Introduction: As artificial intelligence (AI) has become increasingly integrated into daily life, traditional digital literacy frameworks must be revised to address the modern challenges. This study aimed to develop a comprehensive framework that redefines digital literacy in the AI era by focusing on the essential competencies and pedagogical approaches needed in AI-driven education. Methods: This study employed a constructivist and connectivist theoretical approach combined with Jabareen's methodology for a conceptual framework analysis. A systematic literature review from 2010-2024 was conducted across education, computer science, psychology, and ethics domains, using major databases including ERIC, IEEE Xplore, and Google Scholar. The analysis incorporated a modified Delphi technique to validate the framework’s components. Results: The developed framework comprises four key components: technical understanding of AI systems, practical implementation skills, critical evaluation abilities, and ethical considerations. These components are integrated with traditional digital literacy standards through a meta-learning layer that emphasises adaptability and continuous learning. This framework provides specific guidance for curriculum design, pedagogical approaches, assessment strategies, and teacher development. Conclusions: This framework offers a structured approach for reconceptualising digital literacy in the AI era, providing educational institutions with practical guidelines for implementation. Integrating technical and humanistic aspects creates a comprehensive foundation for preparing students for an AI-driven world, while identifying areas for future empirical validation.
This study explores the role of Pancasila education in overcoming the phenomenon of cyberbullying in the digital era, especially during political campaigns. Using a mixed method involving quantitative and qualitative approaches, this study was conducted through a survey of 55 respondents and in-depth interviews to gain a comprehensive understanding. The results showed that 96.8% of respondents were active users of social media, where 61.3% of them admitted to having witnessed cyberbullying, and 11.3% were direct victims. These findings reveal that differences in political views often trigger conflicts that lead to cyberbullying, thus requiring a special approach to maintain national unity.
Although Pancasila education has been taught in the curriculum, 37.1% of respondents felt that this education was still ineffective in preventing negative behavior in cyberspace. 61.3% of respondents supported the implementation of more integrated Pancasila education in various aspects of life, especially in formal education in order to form a polite, tolerant character that respects differences of opinion. In addition, 69.4% of respondents agreed to the integration of Pancasila education into the formal curriculum as a step to build a generation that is critical and responsible in interacting on social media.
This study emphasizes that integrated Pancasila education can play an important role in overcoming cyberbullying in Indonesia. With a more relevant approach to the challenges of the digital era, it is hoped that Pancasila values can be more firmly embedded in society, encouraging the creation of a positive, constructive, and cyberbullying-free digital communication culture.
Keywords: Cyberbullying, Digital Era, Education, Election, Integration, Pancasila Education, Social Media.
This article reviews the origins and use of the terms quasi-experiment and natural experiment. It demonstrates how the terms conflate whether variation in the independent variable of interest falls short of random with whether researchers find, rather than intervene to create, that variation. Using the lens of assignment—the process driving variation in the independent variable—we distinguish three dimensions: control of assignment by the researcher, rather than by a policy process or naturally occurring event; exogeneity of the assignment process; and directness of the link between assignment and the independent variable. These dimensions generate a typology of causal study designs that we illustrate with examples from the social and health sciences. Our framework can assist researchers and practitioners from diverse disciplines to better understand and communicate with one another and, importantly, to recognize opportunities in real-world settings to find or create variation that can be leveraged for valid impact estimates.
This mixed methods study examined the impact of a multi-faceted professional development (PD) program for preschool teachers and classroom assistants on teacher-supported assistive technology (AT) use and early literacy development of children with disabilities. Four special education preschools were randomized into intervention (2 schools, 9 teachers, 50 children) and waitlist control (2 schools, 17 teachers, 42 children) groups. The 24-week PD included online modules, coaching, and AT device kits. Pre-post gains in children's AT use and early literacy skills were analyzed using χ2 and repeated measures ANOVA. Teacher interviews and reflective commentaries were analyzed using Framework Analysis methodology. From pre- to post-test, the percent of children in the intervention group using some form of AT rose from 36 to 80%. The percent of children using AT in the control group went from 45 to 62%. The difference in change between the two groups was statistically significant, χ2 = 13.93, p=.001. Gains in early literacy skills were not significantly different across groups, F(1,90)=0.010, p=.922. Analysis of the qualitative data revealed three themes: the positive impact of AT on child engagement and participation, the importance of individualizing AT for each student, and barriers teachers faced in AT implementation. The PD program had a positive effect on children's AT use but not on gains in early literacy. Teachers' comments highlighted the nuanced relationship between AT use and literacy outcomes, suggesting the need for more targeted implementation of AT during literacy activities.
This study aims to evaluate the impact of digital book usage on students' academic performance in the Advanced Real Analysis course. Employing a quasi-experimental approach with a pretest-posttest control group design, this research involves two groups of students: one group using digital books and the other using conventional printed books. Data were collected through academic performance tests, student satisfaction surveys, and observations of the learning process. The results indicate that students using digital books experienced a significant improvement in academic performance compared to those using printed books. The use of digital books not only enhanced test scores but also provided better access and interactive features that supported a deeper understanding of the material. Satisfaction surveys revealed that students felt digital books facilitated easier access to materials and enhanced the learning process through additional features such as hyperlinks, videos, and animations. Observations also showed that students using digital books displayed higher levels of engagement in class discussions and were more active in exploring additional content. The study concludes that digital books have a significant positive impact on students' academic performance in the Advanced Real Analysis course and recommends integrating digital books into the learning process as an alternative to improve educational effectiveness.
Introduction: Education law plays a pivotal role in shaping educational policies and practices, influencing the equitable distribution of resources and opportunities in educational settings. This study explores the complex interplay between law and education, aiming to analyze legal challenges and their implications for educational equity and access.
Objectives: The research objectives include examining disparities in funding and resources, understanding the legal framework governing special education services, exploring student rights and disciplinary policies, and identifying emerging legal issues in education.
Methodology: A mixed-methods approach, incorporating surveys, interviews, and document analysis, was employed to gather comprehensive data on the subject. Findings: Findings reveal significant disparities in funding and resources, highlighting the urgent need for targeted interventions to address systemic inequalities. The legal framework governing special education services emerges as a critical aspect, emphasizing the importance of ensuring the inclusion of students with disabilities in mainstream educational settings. Furthermore, varying interpretations of student rights and disciplinary policies underscore the complexity of legal challenges facing educators and policymakers.
Conclusion: Analysis of emerging legal issues, such as technology integration and privacy concerns, points to the evolving nature of legal landscapes in education. The study concludes with implications for policy and practice, emphasizing the importance of a nuanced understanding of education law and collaborative efforts to address the complex challenges facing the educational landscape.
Football teams draw the largest crowds of any American collegiate sport, and with them, both positive and negative attention for colleges and universities. Nearly 50 colleges have added the sport recently, but little research has examined the institutional effects of adding a team. Some of these institutions are regional research universities adding the sport as part of broad plans to transform campus identities, while at smaller public and private institutions, adding a football team (with approximately 100 members) appears to be an attempt to boost racial diversity and the number of male students. This study uses difference-in-difference models to find that adding a football team appears to have a significant, but short-term, effect on enrollment and tuition revenue. The long-term effects of adding the sport do not appear to be statistically significant. This raises questions about the costs and benefits of adding football at a time when higher education faces significant challenges attracting students.
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