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Claire McKinley Yoder; Mary Ann Cantrell; Janice L. Hinkle – Journal of School Nursing, 2024
Multiple factors influence a student's success in high school graduation. Individual factors such as disability, racial or ethnic identity, and gender may result in inequity in the school environment, interfering with learning and possibly leading to poorer educational outcomes. This secondary analysis of student educational records (N = 3,782)…
Descriptors: High School Graduates, Graduation Rate, Students with Disabilities, Racial Differences
Darrin DeChane; Takako Nomi; Michael Podgursky – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2024
Like most other states, Missouri uses assessments intended to measure whether students are on a pathway to "college and career readiness." The state longitudinal data system now has the capacity to directly test that claim. We make use of 8th-grade assessment (MAP) scores in Math, Science, and Communication Arts for roughly 260,000…
Descriptors: Grade 8, College Freshmen, Correlation, College Attendance
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Mario I. Suárez; Jason C. Garvey; C. V. Dolan; Musbah Shaheen – AERA Open, 2024
An emerging area of research on the diversification of science, technology, engineering, and mathematics (STEM) is that of queer and trans (QT) students who are interested in STEM education and disciplines. The purpose of our study was to identify differences in high school mathematics educational experiences by sexual orientation and gender…
Descriptors: LGBTQ People, STEM Education, Mathematics, Self Concept
Piper Stanger – ProQuest LLC, 2024
Understanding how to foster belonging and ensure equitable engagement in STEM fields for diverse students is a critical need in education today. Despite substantial growth in the STEM workforce (Bureau of Labor Statistics, 2023; NSF, 2023a), there remain persistent gender and racial/ethnic gaps in students' access to advanced STEM education,…
Descriptors: Sense of Belonging, High School Students, STEM Education, Mathematics Education
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Jason West; Paul Ratanasiripong; Stephen Glass; Christopher Lund – Middle School Journal, 2024
This study investigated the impact middle school tracking has on outcomes in high school and the factors associated with those outcomes. Utilizing data from a large, diverse school district, this quantitative study investigated the relationships between middle school and high school English course enrollment among 4,503 middle school students. The…
Descriptors: Middle School Students, Track System (Education), High School Students, Correlation
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C. J. Appleton; Dara Shifrer; Cesar J. Rebellon – Journal of Early Adolescence, 2024
The literature linking adulthood criminality to cumulative disadvantage and early school misbehavior demonstrates that understanding the mechanisms underlying student behavior and the responses of teachers and administrators is crucial in comprehending racial/ethnic disparities in actual or perceived school misbehavior. We use data on 19,160 ninth…
Descriptors: Data Use, Racial Differences, Behavior Problems, Student Behavior
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E. Thomsen; M. Henderson; A. Moore; N. Price; M. W. McGarrah – National Center for Education Statistics, 2024
The tables in this report provide national-level estimates of the extent to which students ages 12-18 enrolled in grades 6-12 experience bullying during school. The tables show how bullying victimization varies by student and school characteristics such as sex, race/ethnicity, grade, household income, region, school locale, school enrollment size,…
Descriptors: Bullying, Victims, Grade 6, Grade 7
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Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students