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Richard J. Murnane; John B. Willett; Aubrey McDonough; John P. Papay; Ann Mantil – AERA Open, 2024
The labor-market payoff to workers with associate degrees in healthcare and STEM occupations is very high in Massachusetts. We examine whether this induced a growing proportion of students in MA community colleges (MACCs) to earn an associate degree (AD) in one of these fields. We do this by using multinomial logit analysis to compare trends…
Descriptors: Community College Students, Associate Degrees, Allied Health Occupations, STEM Careers
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
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
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