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Krista M. Soria; Stacey E. Vakanski; Trevor White; Ryan Arp – Community College Review, 2024
Objective: The purpose of this paper was to examine variables associated with food insecurity among community college caregivers during the COVID-19 pandemic. Methods: We used data from a multi-institutional survey of 15,051 caregivers enrolled at 130 community colleges in 42 states in fall 2020. We used a logistic regression to examine whether…
Descriptors: Food, Hunger, Community Colleges, Caregivers
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Harun Cigdem; Semiral Oncu – TechTrends: Linking Research and Practice to Improve Learning, 2025
Despite efforts to implement innovative approaches such as flipped learning leveraging computer technology, the challenge of student failure persists. Understanding the factors that contribute to student success in flipped engineering courses remains a critical issue. This study addresses this issue by investigating the impact of student…
Descriptors: Gamification, Flipped Classroom, Learner Engagement, Learning Readiness
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Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
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Oscar A. Aliaga; Pradeep Kotamraju – International Journal of Adult Education and Technology, 2024
We examined the impact of secondary Career and Technical Education (CTE) credit-taking on students attaining full-time employment following graduation from high school in the United States, and whether such credit-taking impacted students entering employment in a related field. We focused on occupational programs, and we used four levels of CTE…
Descriptors: Vocational Education, Credits, Employment, Secondary Education
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Munise Seçkin Kapucu; I?brahim Özcan; Hülya Özcan; Ahmet Aypay – International Journal of Technology in Education and Science, 2024
Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…
Descriptors: Secondary School Students, Science Achievement, Artificial Intelligence, Foreign Countries
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Amy Overbay; Christopher W. Thurley – Writing Center Journal, 2024
As institutions cope with the difficult task of managing scarce resources to support student learning, college writing centers, like other student services, need to be able to articulate and, at times, quantify the benefits they offer the populations they serve. This study examined outcomes associated with visiting the writing center at one…
Descriptors: Community Colleges, Laboratories, Writing (Composition), Academic Achievement
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Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
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Tisha L. N. Emerson; KimMarie McGoldrick – Journal of Economic Education, 2024
Using data from 11 institutions, the authors investigate enrollments in intermediate microeconomics to determine characteristics of successful and unsuccessful students and follow the retake behavior of unsuccessful students. Successful students are significantly different from unsuccessful ones, and unsuccessful students differ by type…
Descriptors: Microeconomics, Student Attrition, Withdrawal (Education), Academic Persistence
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Nirmal Ghimire; Kouider Mokhtari – AERA Online Paper Repository, 2024
This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
Joao M. Souto-Maior; Kenneth A. Shores; Rachel E. Fish – Annenberg Institute for School Reform at Brown University, 2025
Whether selection processes contribute to group-level disparities or merely reflect pre-existing inequalities is an important societal question. In the context of observational data, researchers, concerned about omitted-variable bias, assess selection-contributing inequality via a kitchen-sink approach, comparing selection outcomes of…
Descriptors: Control Groups, Predictor Variables, Correlation, Selection Criteria
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Syahrul Amin; Karen E. Rambo-Hernandez; Blaine A. Pedersen; Camille S. Burnett; Bimal P. Nepal; Noemi V. Mendoza Diaz – Cogent Education, 2024
This study examined the persistence of first-year engineering students at a Hispanic-Serving Institution (HSI) and a Historically Black College and University (HBCU) pre- and mid-COVID-19 interruptions and whether their characteristics (race/ethnicity, financial need status, first-generation status, SAT scores) predicted their persistence. Using…
Descriptors: College Freshmen, Engineering Education, Academic Persistence, COVID-19
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
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Ndudi O. Ezeamuzie; Jessica S. C. Leung; Dennis C. L. Fung; Mercy N. Ezeamuzie – Journal of Computer Assisted Learning, 2024
Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational…
Descriptors: Educational Policy, Predictor Variables, Computation, Thinking Skills
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Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
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Cynthia J. Murphy; Siffat A. Sharmin; Hsien-Yuan Hsu – Journal of Education for Students Placed at Risk, 2024
Although studies have investigated educational attainment of groups of students professing low and high educational self-expectations, groups of noncommittal students, rather than being studied as a discrete group, have been treated as missing and ignored. This study investigated the differences between students of noncommittal, low, and high…
Descriptors: Grade 10, Educational Attainment, Hispanic American Students, African American Students