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Jason Willard King – ProQuest LLC, 2024
The Great Mountain High School (GMHS) started a program to help support students at risk for not graduating high school. The focus of this study was to provide a formative program evaluation of the created program that (a) investigated the fidelity of implementation of the activities and processes of the program, (b) gathered an understanding of…
Descriptors: High School Seniors, Grade 12, At Risk Students, Dropout Prevention
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Robin Clausen – Discover Education, 2025
Early Warning Systems (EWS) are research-based analytics that use statistical models to assess dropout risk. School leaders use this analytic to consolidate data about a student and provide actionable data to craft an intervention. Little is currently known about the processes involved in school implementation or data use. By analyzing Montana EWS…
Descriptors: Dropout Prevention, Data Analysis, Principals, School Counselors
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Hank S. Bohanon; Meng-Jia Wu; Ali Kushki; Cheyne LeVesseur – Australasian Journal of Special and Inclusive Education, 2024
Schools have an increased focus on implementing schoolwide initiatives (e.g., multi-tiered systems of support; MTSS) to address risk factors related to dropping out. These interventions can involve multiple domains, including academic, behavioural, and social and emotional supports. Although researchers suggest that schoolwide interventions are…
Descriptors: High Schools, Educational Change, Multi Tiered Systems of Support, Academic Achievement
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Robin Clausen – AASA Journal of Scholarship & Practice, 2024
Policy research established that it is possible to predict a student will drop out of school based on academic, attendance, behavior indicators. Little is known about the processes that put Early Warning Systems (EWS) in place. This case study of the Montana EWS describes the characteristics of a statewide implementation, the efficiency of the EWS…
Descriptors: Dropout Prevention, High School Students, Graduation, Graduation Rate
Jeremiah T. Stark – ProQuest LLC, 2024
This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…
Descriptors: High School Students, Graduation Rate, Predictor Variables, Predictive Validity
Julia Porter Hilbert – ProQuest LLC, 2024
A history of trauma can have a significant negative impact on the functioning of a student in the school environment. Check & Connect, a school engagement and dropout intervention program, has been found to have positive effects on student school engagement and decrease the likelihood of dropping out of school. However, a review of the Check…
Descriptors: Trauma, Dropout Prevention, Program Effectiveness, Intervention
Morgan A. Parker Money – ProQuest LLC, 2024
This study centered student perspectives from one high school in Southwest Washington on push out and push in factors related to high school leaving. While there has been significant research conducted on risk factors associated with early school leaving, there has been very little published research that centers student voice and their…
Descriptors: High School Students, Dropout Attitudes, Dropout Prevention, Potential Dropouts