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This is a protocol for a Campbell systematic review. The objectives are to synthesize published and unpublished scientific literature on (1) the prevalence of and (2) the risk and protective factors for school‐aged youth radicalization. In the nascent field of research on radicalized youth, a priority of the review is to examine the breadth of empirical evidence on prevalence and risk and protective factors, and to identify gaps and opportunities for further research. Our research questions are: Part 1: Prevalence: What is the prevalence of radicalization among school‐aged youth? How does the prevalence of radicalization of school‐aged youth vary over time and by type of radical ideology (e.g., religiously motivated, far left and far right, ethno‐nationalist, racially and ethnically motivated, gender‐based, online conspiracy‐based)? Part 2: Risk and Protective Factors What are the cognitive and behavioral risk and protective factors for school‐aged youth radicalization? How do the cognitive and behavioral risk and protective factors for youth radicalization vary by type of radical ideology (e.g., religiously motivated, far left and far right, ethno‐nationalist, racially and ethnically motivated, gender‐based, online conspiracy‐based)?

Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework.

Background School‐based law enforcement (SBLE) has become a common intervention. Although SBLE is meant to make schools safer, critics suggest it may not accomplish this purpose, and may have unintended negative consequences such as increasing students’ exclusionary discipline or contact with the criminal justice system. There may also be secondary effects related to perceptions of the school or student learning. Objectives The purpose of this review is to synthesize the literature evaluating the use of SBLE, including outcomes related to (a) crime and behavior problems; (b) perceptions of safety; and (c) learning. Methods We conducted a systematic literature search to identify studies that examined outcomes associated with SBLE use. Eligible studies used experimental or quasi‐experimental designs; included samples of students, teachers/staff, schools, or school districts; reported on a policing strategy focused on crime prevention or school safety that did not involve officers teaching a curriculum; included a measure that reflects crime and behavior problems, perceptions of safety, or learning; and were in a primary or secondary school. Following a multi‐stage screening process to identify studies eligible for inclusion, we estimated a series of meta‐analytic models with robust variance estimation to calculate weighted mean effect sizes for each of three main categories of outcomes and commonly occurring subsets of these categories. We examined heterogeneity in these estimates across features of the primary studies’ design. Results The search and screening process yielded 1002 effect sizes from 32 reports. There were no true experiments, and the quasi‐experiments ranged from strictly correlational to permitting stronger causal inferences. SBLE use was associated with greater crime and behavior problems in studies that used schools as the unit of analysis. Within this category, SBLE use was associated with increased exclusionary discipline among studies that used both schools (g = 0.15, 95% confidence interval [CI] [0.02, 0.27]) and students (g = 0.003, 95% CI [0.002, 0.003]) as the unit of analysis. SBLE use was not associated with any measures of crime or violence in schools. SBLE use was associated with greater feelings of safety among studies that used schools as the unit of analysis (g = 0.18, 95% CI [0.13, 0.24]), although this estimate was based on only seven effect sizes from two correlational studies. All the other models, including those examining learning outcomes, yielded null results. None of the moderators tested showed meaningful relationships, indicating the findings were consistent across a variety of study design features. Authors’ Conclusions This study's findings provide no evidence that there is a safety‐promoting component of SBLE, and support the criticism that SBLE criminalizes students and schools. Although we found no evidence of differences across methodological features, risk of bias in the primary studies limits our confidence in making causal inferences. To the extent that the findings are causal, schools that invest in strategies to improve safety will likely benefit from divesting from SBLE and instead investing in evidence‐based strategies for enhancing school safety. Schools that continue to use SBLE should ensure that their model has no harmful effects and is providing safety benefits.

Although data modeling, the employment of statistical reasoning for the purpose of investigating questions about the world, is central to both mathematics and science, it is rarely emphasized in K-16 instruction. The current work focuses on developing thinking about data modeling with undergraduates in general and preservice teachers in particular. Subjects were 125 undergraduate preservice teachers (118 females) from a highly selective, nationally recognized teacher education program. A design-based research methodology was used, and data analysis took the form of retrospective, cross-iteration comparisons where themes of the design of inquiry, measurement, deficit model of experimentation, and epistemology and nature of science emerged. Our findings are relevant to those who seek ways to support undergraduate understanding of statistical reasoning as well as making a contribution to the challenging problem of how to support and integrate preservice teachers' coordination of data modeling and inquiry into their pedagogical practice.

This paper describes the design, development, implementation, and assessment of a multimedia‐based learning module focused on biomechanics. The module is comprised of three challenges and is based on a model of learning and instruction known as the How People Learn (HPL) framework. Classroom assessment of the first challenge was undertaken to test the hypothesis that the HPL approach increases adaptive expertise in movement biomechanics. Student achievement was quantified using pre‐ and post‐test questionnaires designed to measure changes in three facets of adaptive expertise: factual and conceptual knowledge and transfer. The results showed that the HPL approach increased students' conceptual knowledge as well as their ability to transfer knowledge to new situations. These findings indicate that challenge‐based instruction, when combined with an intellectually engaging curriculum and principled instructional design, can accelerate the trajectory of novice to expert development in bioengineering education.

We present results of an investigation of preservice secondary mathematics and science teachers' conceptions of project-based instruction (PBI) and their enactments of PBI in apprentice (student) teaching. We evaluated their thinking and implementations within a composite framework based on the work of education researchers. We analyzed survey responses, both qualitatively and statistically, from three cohorts of preservice teachers both before and after apprentice teaching. In addition we interviewed and observed a subset of these future teachers. We found that in general the preservice teachers held superficial views of PBI, as compared to the researcher framework. Participants reported time and curriculum restrictions as major barriers; however, teachers for whom enactment of PBI was presented as an explicit goal, and who were given support toward that end, were more likely to enact authentic implementations, regardless of previous reservations about PBI. Without this additional scaffolding, even teachers with high affinity for PBI were unlikely to implement it authentically.

Background Previous work found four areas critical to understanding evolution: variation, selection, inheritance, and deep time. Methods An exploratory qualitative approach was taken with a variety of data sources from a larger data corpus. Data were analyzed for emphasis of either decentralized or centralized thinking. Data were analyzed and discussed exploring how a group of high school biology teachers from the same department taught evolutionary concepts. Results The paper presents evidence that demonstrates a common lack of thinking from this perspective or incorrectly thinking that evolutionary processes are “driven” by some centralized force. Conclusions We now identify a critical fifth component: decentralized mindset or thinking of evolution as a complex system. Possibilities of how this new area can affect learning about evolution are discussed and implications for assessment are also discussed.

This article responds to arguments by Skidmore and Thompson (this issue of Educational Researcher) that a graph published more than 10 years ago was erroneously reproduced and \"gratuitously damaged\" perceptions of the quality of education research. After describing the purpose of the original graph, the author counters assertions that the graph changed perceptions or that this was anything more than a case of unintentional editorial sloppiness.