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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
Yamamoto, Scott H.; Alverson, Charlotte Y. – Autism & Developmental Language Impairments, 2022
Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and…
Descriptors: Autism Spectrum Disorders, Students with Disabilities, High School Graduates, Outcomes of Education
Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
Baker, Ryan S.; Berning, Andrew W.; Gowda, Sujith M.; Zhang, Shizhu; Hawn, Aaron – Journal of Education for Students Placed at Risk, 2020
Dropout remains a persistent challenge within high school education. In this paper, we present a case study on automatically detecting whether a student is at-risk of dropout within a diverse school district in Texas. We predict whether a student will drop out in a future school year from data on students' discipline, attendance, course-taking,…
Descriptors: At Risk Students, High School Students, Dropout Prevention, Student Diversity
Alkharusi, Hussain – Electronic Journal of Research in Educational Psychology, 2016
Introduction: Students are daily exposed to a variety of assessment tasks in the classroom. It has long been recognized that students' perceptions of the assessment tasks may influence student academic achievement. The present study aimed at predicting academic achievement in mathematics from perceptions of the assessment tasks after controlling…
Descriptors: Academic Achievement, Predictive Measurement, Predictor Variables, Learning Strategies
West, Stephen G.; Hughes, Jan N.; Kim, Han Joe; Bauer, Shelby S. – Educational Measurement: Issues and Practice, 2019
The Motivation for Educational Attainment (MEA) questionnaire, developed to assess facets related to early adolescents' motivation to complete high school, has a bifactor structure with a large general factor and three smaller orthogonal specific factors (teacher expectations, peer aspirations, value of education). This prospective validity study…
Descriptors: Student Motivation, Educational Attainment, Questionnaires, Adolescent Attitudes
Berlin, Noémi; Tavani, Jean-Louis; Beasançon, Maud – Education Economics, 2016
We investigate the link between schooling achievement and creativity scores, controlling for personality traits and other individual characteristics. Our study is based on field data collected in a secondary school situated in a Parisian suburb. Four scores of creativity were measured on 9th graders. Verbal divergent thinking negatively predicts…
Descriptors: Creativity, Academic Achievement, Scores, Personality Traits
Weybright, Elizabeth H.; Caldwell, Linda L.; Xie, Hui; Wegner, Lisa; Smith, Edward A. – South African Journal of Education, 2017
Education is one of the strongest predictors of health worldwide. In South Africa, school dropout is a crisis where by Grade 12, only 52% of the age appropriate population remain enrolled. Survival analysis was used to identify the risk of dropping out of secondary school for male and female adolescents and examine the influence of substance use…
Descriptors: Foreign Countries, Predictor Variables, Predictive Measurement, Secondary School Students
Peters, S. Colby; Woolley, Michael E. – Children & Schools, 2015
Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…
Descriptors: Academic Achievement, Success, Risk, Risk Assessment
Legewie, Joscha; DiPrete, Thomas A. – Sociology of Education, 2014
Despite the striking reversal of the gender gap in education, women pursue science, technology, engineering, and mathematics (STEM) degrees at much lower rates than those of their male peers. This study extends existing explanations for these gender differences and examines the role of the high school context for plans to major in STEM fields.…
Descriptors: High School Students, Gender Differences, Achievement Gap, Educational Environment
Bozick, Robert; Gonzalez, Gabriella; Engberg, John – Journal of Student Financial Aid, 2015
The Pittsburgh Promise is a scholarship program that provides $5,000 per year toward college tuition for public high school graduates in Pittsburgh, Pennsylvania who earned a 2.5 GPA and a 90% attendance record. This study used a difference-in-difference design to assess whether the introduction of the Promise scholarship program directly…
Descriptors: Merit Scholarships, College Bound Students, Enrollment Influences, Enrollment Management
Cratty, Dorothyjean – Economics of Education Review, 2012
Nineteen percent of 1997-98 North Carolina 3rd graders were observed to drop out of high school. A series of logits predict probabilities of dropping out on determinants such as math and reading test scores, absenteeism, suspension, and retention, at the following grade levels: 3rd, 5th, 8th, and 9th. The same cohort and variables are used to…
Descriptors: At Risk Students, Dropouts, High School Students, Probability
Flores, Raymond; Inan, Fethi; Lin, Zhangxi – Journal of Computers in Mathematics and Science Teaching, 2013
In this study, the National Educational Longitudinal Study (ELS:2002) dataset was used and a predictive data mining technique, decision tree analysis, was implemented in order to examine which factors, in conjunction to computer use, can be used to predict high or low probability of success in high school mathematics. Specifically, this study…
Descriptors: Educational Technology, Computer Uses in Education, Longitudinal Studies, Predictor Variables
Cawthon, Stephanie W.; Caemmerer, Jacqueline M.; Dickson, Duncan M.; Ocuto, Oscar L.; Ge, Jinjin; Bond, Mark P. – Applied Developmental Science, 2015
Social skills function as a vehicle by which we negotiate important relationships and navigate the transition from childhood into the educational and professional experiences of early adulthood. Yet, for individuals who are deaf, access to these opportunities may vary depending on their preferred language modality, family language use, and…
Descriptors: Predictor Variables, Prediction, Predictive Measurement, Predictive Validity
Onder, Fulya Cenkseven; Yilmaz, Yasin – Educational Sciences: Theory and Practice, 2012
The purpose of this study is to determine whether the parenting styles and life satisfaction predict delinquent behaviors frequently or not. Firstly the data were collected from 471 girls and 410 boys, a total of 881 high school students. Then the research was carried out with 502 students showing low (n = 262, 52.2%) and high level of delinquent…
Descriptors: High School Students, Measures (Individuals), Parenting Styles, Delinquency
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