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Murata, Ryusuke; Okubo, Fumiya; Minematsu, Tsubasa; Taniguchi, Yuta; Shimada, Atsushi – Journal of Educational Computing Research, 2023
This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with "an RNN model with a long-term time-series in which the features during the entire course…
Descriptors: College Students, Academic Achievement, Prediction, Neurology
Jones, Raytosha – ProQuest LLC, 2023
This study will examine the first-to-second-year college persistence of students who graduated from Fort Worth Independent School District (FWISD) in the class of 2018. The study seeks to understand how the attainment of college readiness indicators correlates with college persistence in two categories of students, resourced and under-resourced.…
Descriptors: Academic Persistence, College Students, College Readiness, State Standards
Jair Munoz – ProQuest LLC, 2023
Stemmed from zero-tolerance policies, Disciplinary Alternative Education Programs/Classrooms (DAEPs) in Texas are disciplinary spaces designed to house students deemed at risk, while schools continue to serve students' educational needs of (Aron 2006, Tajalli & Garba, 2014). Once there, students are stereotyped with analogous carceral-framed…
Descriptors: Nontraditional Education, Nontraditional Students, Discipline, Zero Tolerance Policy
Asaad, Marina – ProQuest LLC, 2023
The purpose of this study was to determine changes noted in at-risk students following the implementation of at-risk students of all ages. The study assessed the impact of mentorship on at-risk students' behavior, confidence, and self-efficacy. Qualitative and quantitative analyses were examined, and pre- and post-data were collected and analyzed.…
Descriptors: At Risk Students, Mentors, Program Effectiveness, Student Behavior
Sarker Monojit Asish – ProQuest LLC, 2023
Virtual Reality (VR) has been found useful to improve engagement and retention level of students, for some topics, compared to traditional learning tools such as books, and videos. However, a student could still get distracted and disengaged due to a variety of factors including stress, mind-wandering, unwanted noise, external alerts, and internal…
Descriptors: Students, Attention Control, Computer Simulation, Artificial Intelligence
Kelli Bird – Association for Institutional Research, 2023
Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…
Descriptors: Prediction, Data Analysis, Artificial Intelligence, Identification
Erin Barno – ProQuest LLC, 2025
This dissertation investigates how early career teachers' orientations of students and their mathematical activity, as surfaced their experience in a simulation, can reveal when their well-intentioned decisions might negatively impact traditionally underserved students. Three early career teachers engaged in two online simulations where they could…
Descriptors: Beginning Teachers, Mathematics Instruction, Computer Simulation, Vignettes
Robin Busse; Elisabeth Maué – Vocations and Learning, 2025
Preparing youth for the transition from school to work is a key labor market challenge in many countries. Across Europe, there are many educational programs to improve youth's school-to-work transitions. In Germany, youth with difficulties in their school-to-work transitions are channeled into prevocational programs to help them to enter…
Descriptors: Foreign Countries, Career and Technical Education, Education Work Relationship, Internship Programs
Joseph R. Cimpian; Mollie T. McQuillan – Educational Researcher, 2025
Recent data show rising suicidality among high school girls. We posit this increase may be related to an overlooked factor: more girls identifying as LGBQ. Using four cohorts of national Youth Risk Behavior Survey data (N = 22,562 females, N = 22,130 males), we found that LGBQ identification among females rose from 15% in 2015 to 34% in 2021. LGBQ…
Descriptors: High School Students, Gender Differences, LGBTQ People, At Risk Students
Caroline Mierzwa; Nathaniel von der Embse; Eunsook Kim; Melissa Brown – Journal of Psychoeducational Assessment, 2025
Unaddressed social, emotional, and behavioral (SEB) needs and academic challenges may lead to negative youth outcomes. Universal behavioral risk screeners, like the student self-report Social, Academic, and Emotional Behavior Risk Screener (SAEBRS-SRS), identify at-risk students. To improve screening tool use, research is needed to identify the…
Descriptors: Psychological Patterns, Prediction, Academic Achievement, Screening Tests
Raymond Brown; Z. W. Taylor – Journal of The First-Year Experience & Students in Transition, 2025
At many institutions of higher education, first-year seminars are meant to help students transition to college life and to provide them with resources and skills to develop socially and academically, supporting persistence (Jamelske, 2009; Janz, 2004). Since their inception, researchers have evaluated the effectiveness of these seminars and their…
Descriptors: First Year Seminars, College Students, Program Effectiveness, Student Adjustment
Megan N. Imundo; Maria Goldshtein; Micah Watanabe; Jiachen Gong; Devon Nicole Crosby; Rod D. Roscoe; Tracy Arner; Danielle S. McNamara – Grantee Submission, 2025
Introduction: Student retention is a critical issue in higher education. Universities have responded by implementing supports like early alert systems. Objective: We investigated students' knowledge of and experiences with an early alert system designed to enhance academic persistence. Method: We surveyed (N = 356) undergraduates at a large public…
Descriptors: Academic Persistence, At Risk Students, Identification, School Holding Power
Gurvinder Kaur; Stephanie Stroever; Megh Gore; Bridget Vories; Vaughan H. Lee; Keith N. Bishop; Brandt L. Schneider – Discover Education, 2025
Background: Formative assessments build a positive learning environment and provide feedback to enhance learning. This study examined the impact of online formative and low-stake summative assessments on medical students' learning outcomes in the Clinically Oriented Anatomy course from 2016 to 2020. We aimed to demonstrate that formative…
Descriptors: At Risk Students, Identification, Prediction, Anatomy
Anca Muresan; Mihaela Cardei; Ionut Cardei – International Educational Data Mining Society, 2025
Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on-time graduation. In educational settings, AI-powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to…
Descriptors: Artificial Intelligence, At Risk Students, Learning Analytics, Technology Uses in Education
Anne Barwasser; Hannah Skrobek; Matthias Grünke – International Journal for Research in Learning Disabilities, 2025
The number of students who are deficient in literacy is increasing worldwide, possibly exacerbated by concurrent behavioral difficulties. This international challenge leads to the question of how and when to support children. Support should start in kindergarten and be focused on the acquisition of phonological awareness. Based on this premise, we…
Descriptors: Phonological Awareness, Training, Kindergarten, Behavior Problems

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