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Mohammad Arif Ul Alam; Madhavi Pagare; Susan Davis; Geeta Verma; Ashis Biswas; Justin Barbern – International Educational Data Mining Society, 2024
Recognizing the Social Determinants of Mental Health (SDMHs) among students is essential, as lower backgrounds in these determinants elevate the risk of poor academic achievement, behavioral issues, and physical health problems, thereby affecting both physical and emotional well-being. Leveraging students' self-reported lived experiential essays…
Descriptors: Mental Health, At Risk Students, Prediction, Automation
Brooke Wilkins – Phi Delta Kappan, 2025
An efficient assessment cycle is a necessary component of early literacy instruction. To support student growth, educators must screen, diagnose, and monitor student progress. Diagnostic assessments provide critical information that can empower educators to address student needs. Using information from diagnostic assessments requires educators to…
Descriptors: Diagnostic Tests, Data Use, Emergent Literacy, Models
Amy Werpy; Bonnie Brett; Johann Liljengren – Colorado Department of Education, 2025
The Expelled and At-Risk Student Services (EARSS) program provides funding to implement educational and support services to expelled students, students at risk of being expelled, chronically absent and/or habitually truant students. Funds are annually appropriated to the Colorado Department of Education (CDE) for the purpose of making grants…
Descriptors: Expulsion, At Risk Students, Truancy, State Aid
Chaewon Lee; Lan Luo; Shelbi L. Kuhlmann; Robert D. Plumley; Abigail T. Panter; Matthew L. Bernacki; Jeffrey A. Greene; Kathleen M. Gates – Journal of Learning Analytics, 2025
The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…
Descriptors: Electronic Learning, Prediction, Data Analysis, Artificial Intelligence
Jose Silva-Lugo; Heather Maness – Sage Research Methods Cases, 2025
The study provides a detailed methodological approach, cross-industry standard process for data mining, for predicting at-risk students with an imbalanced class. The objective was to identify the best machine learning model for predicting students at risk of failing the course during weeks 2-8 of the semester. We encountered issues in the dataset,…
Descriptors: Prediction, Predictor Variables, At Risk Students, Information Retrieval
Beulah San Juanita Rangel – ProQuest LLC, 2024
Rangel, Beulah S. J., At Risk, Punished, and Pushed out of School: Leadership Practices Utilized by School Administrators for Discipline Consequences in South Texas Schools. Doctor of Education (Ed. D.), May 2024The purpose of this study was to examine leadership practices utilized by school administrators for discretionary discipline consequences…
Descriptors: At Risk Students, Principals, Administrator Attitudes, Leadership Styles
C. Rashaad Shabab – Teaching Mathematics and Its Applications, 2024
This paper applies the well-known cognitive bias of loss aversion from behavioural economics to student decisions over engagement with mathematically demanding coursework. This bias is shown to predict behaviour that is consistent with mathematics anxiety in a dynamic model of student engagement. It is shown that these forces can imply…
Descriptors: Mathematics Anxiety, Mathematics Instruction, Difficulty Level, Student Behavior
Han Bum Lee; Michael U. Villarreal – Journal of Education for Students Placed at Risk, 2023
This study examined the effect of dual enrollment (DE) on college enrollment and degree completion for students with lower prior academic achievement who attended public high schools in Texas. We employed a propensity score matching method to reduce selection bias arising from DE participation and supplemented the analysis with a bounds test. The…
Descriptors: At Risk Students, Dual Enrollment, Low Achievement, High School Students
Gila Apelboim-Dushnitzky; Adina Shamir – European Journal of Special Needs Education, 2025
First graders with Developmental Language Disorder are considered at risk for exhibiting Specific Learning Disorder during school years. They also have deficiencies in their metacognitive skills, which leads to less effective learning processes. The current study examined, for the first time, the added value of various types of metacognitive…
Descriptors: Emergent Literacy, Children, At Risk Students, Learning Disabilities
Victoria E. Goldman; Jacqueline Antoun; Panteha Hayati Rezvan; Kevin Fang – Journal of School Health, 2025
Background: School absenteeism and health have a close bidirectional link: children with medical conditions miss more school, and chronic absenteeism is tied to poor health outcomes. Despite rising absenteeism, tools to assess school attendance in healthcare settings remain underexplored. Brief, standardized attendance screening may help address…
Descriptors: Attendance, Attendance Patterns, Screening Tests, Special Health Problems
Xiaona Xia; Wanxue Qi – Routledge, Taylor & Francis Group, 2025
This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior. In order to flexibly perceive and intervene in the "burnout state" and improve online learning…
Descriptors: Burnout, Online Courses, Technology Uses in Education, Prevention
Tracy L. Cross – Gifted Child Today, 2024
The author focuses on positive psychology as an important approach to supporting the psychological well-being of students with gifts and talents. Research has identified protective factors that can counteract risk factors for suicidal behavior. These protective factors may be found within the individual, the family, peers, the school, the…
Descriptors: Student Welfare, Gifted, At Risk Students, Suicide
Gabriella M. Sallai; Catherine G. P. Berdanier – Journal of Engineering Education, 2024
Background: Although most engineering graduate students are funded and usually complete their degrees faster than other disciplines, attrition remains a problem in engineering. Existing research has explored the psychological and sociological factors contributing to attrition but not the structural factors impacting attrition. Purpose/Hypothesis:…
Descriptors: Engineering Education, Student Attrition, Dropouts, Dropout Characteristics
Xin Qiao; Akihito Kamata; Cornelis Potgieter – Grantee Submission, 2024
Oral reading fluency (ORF) assessments are commonly used to screen at-risk readers and evaluate interventions' effectiveness as curriculum-based measurements. Similar to the standard practice in item response theory (IRT), calibrated passage parameter estimates are currently used as if they were population values in model-based ORF scoring.…
Descriptors: Oral Reading, Reading Fluency, Error Patterns, Scoring
Michael Rothman – ProQuest LLC, 2024
A growing body of research distinguishes between official systems of accountability and "felt" accountability - how people feel responsible to one another across a web of complex, intersecting relationships. Research in the behavioral sciences suggests that while such feelings have a dramatic effect on results, these have been…
Descriptors: High Schools, Nontraditional Education, At Risk Students, Accountability

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