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Showing 1 to 15 of 77 results Save | Export
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Yongtian Cheng; K. V. Petrides – Educational and Psychological Measurement, 2025
Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…
Descriptors: Psychological Studies, Artificial Intelligence, Cognitive Processes, Predictive Validity
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Jacqueline M. Caemmerer; Stephanie Ruth Young; Danika Maddocks; Natalie R. Charamut; Eunice Blemahdoo – Journal of Psychoeducational Assessment, 2024
In order to make appropriate educational recommendations, psychologists must understand how cognitive test scores influence specific academic outcomes for students of different ability levels. We used data from the WISC-V and WIAT-III (N = 181) to examine which WISC-V Index scores predicted children's specific and broad academic skills and if…
Descriptors: Predictor Variables, Academic Achievement, Intelligence Tests, Children
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Seungbak Lee; Minsoo Kang; Jae-Hyeon Park; Hyo-Jun Yun – Measurement in Physical Education and Exercise Science, 2025
The PageRank model has been applied in sport ranking systems; however, prior implementations exhibited limitations and failed to produce valid rankings. This study analyzed 1,466 National Collegiate Athletic Association (NCAA) Division 1 football games and developed a novel, modified PageRank model. We also proposed an artificial…
Descriptors: Algorithms, Evaluation Methods, Team Sports, College Athletics
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Buckley, Jeffrey – International Journal of Technology and Design Education, 2022
General intelligence is a fundamental human capacity with significant educational implications. However, it is often not considered in educational research despite substantial evidence illustrating its association with positive life outcomes and student's capacity to learn. There are a number of potential reasons for this including the…
Descriptors: Intelligence, Design, Technology Education, Educational Research
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Yijun Zhao; Zhengxin Qi; Son Tung Do; John Grossi; Jee Hun Kang; Gary M. Weiss – International Educational Data Mining Society, 2024
GRE Aptitude Test scores have been a key criterion for admissions to U.S. graduate programs. However, many universities lifted their standardized testing requirements during the COVID-19 pandemic, and many decided not to reinstate them once the pandemic ended. This change poses additional challenges in evaluating prospective students. In this…
Descriptors: College Entrance Examinations, Graduate Study, Scores, College Applicants
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Leonie Fleck; Dorothee Amelung; Anna Fuchs; Benjamin Mayer; Malvin Escher; Lena Listunova; Jobst-Hendrik Schultz; Andreas Möltner; Clara Schütte; Tim Wittenberg; Isabella Schneider; Sabine C. Herpertz – Advances in Health Sciences Education, 2025
Doctors' interactional competencies play a crucial role in patient satisfaction, well-being, and compliance. Accordingly, it is in medical schools' interest to select candidates with strong interactional abilities. While Multiple Mini Interviews (MMIs) provide a useful context to assess such abilities, the evaluation of candidate performance…
Descriptors: Medical Students, Medical Schools, College Admission, Admission Criteria
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Xiaoyu Tang; Yayun Gong; Yang Xiao; Jianwen Xiong; Lei Bao – Journal of Science Education and Technology, 2025
Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students' emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning…
Descriptors: Physics, Science Instruction, Nonverbal Communication, Science Achievement
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Jorge López González; Jesús Manuel Martínez; Maven Lomboy; Luis Expósito – Cogent Education, 2024
This article examines the relationship between emotional intelligence and ethical leadership competencies among university students. The research hypothesis was that emotional intelligence correlates positively with the exercise of good leadership. To this aim, a study was carried out with 1101 university students from Chile, Mexico and Spain who…
Descriptors: Emotional Intelligence, Leadership Qualities, Competence, College Students
Jing Liu; Megan Kuhfeld; Monica Lee – Annenberg Institute for School Reform at Brown University, 2023
Noncognitive constructs such as self-efficacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by…
Descriptors: Student Behavior, Predictor Variables, Predictive Validity, Academic Achievement
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Yik, Brandon J.; Dood, Amber J.; Cruz-Ramirez de Arellano, Daniel; Fields, Kimberly B.; Raker, Jeffrey R. – Chemistry Education Research and Practice, 2021
Acid-base chemistry is a key reaction motif taught in postsecondary organic chemistry courses. More specifically, concepts from the Lewis acid-base model are broadly applicable to understanding mechanistic ideas such as electron density, nucleophilicity, and electrophilicity; thus, the Lewis model is fundamental to explaining an array of reaction…
Descriptors: Artificial Intelligence, Models, Formative Evaluation, Organic Chemistry
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Montero, Shirly; Arora, Akshit; Kelly, Sean; Milne, Brent; Mozer, Michael – International Educational Data Mining Society, 2018
Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network…
Descriptors: Artificial Intelligence, Individualized Instruction, Knowledge Level, Bayesian Statistics
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Wu, Jiun-Yu; Hsiao, Yi-Cheng; Nian, Mei-Wen – Interactive Learning Environments, 2020
This paper demonstrated the use of the supervised Machine Learning (ML) for text classification to predict students' final course grades in a hybrid Advanced Statistics course and exhibited the potential of using ML classified messages to identify students at risk of course failure. We built three classification models with training data of 76,936…
Descriptors: Social Media, Discussion Groups, Artificial Intelligence, Classification
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Klieger, David; Bochenek, Jennifer; Ezzo, Chelsea; Holtzman, Steven; Cline, Frederick; Olivera-Aguilar, Margarita – International Journal of Testing, 2022
Consideration of socioemotional skills in admissions potentially can increase representation of racial and ethnic minorities and women in graduate and professional education as well as identify candidates more likely to succeed in graduate and professional school. Research on one such assessment, the ETS Personal Potential Index (PPI), showed that…
Descriptors: College Admission, Graduate Study, Professional Education, Interpersonal Competence
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Bacon, Elizabeth C.; Moore, Adrienne; Lee, Quimby; Carter Barnes, Cynthia; Courchesne, Eric; Pierce, Karen – Autism: The International Journal of Research and Practice, 2020
While many children with autism spectrum disorder are now detected at young ages given the rise in screening and general awareness, little is known regarding the prognosis of early detected children. The brain is shaped by experience-dependent mechanisms; thus, what a child pays attention to plays a pivotal role in shaping brain development. Eye…
Descriptors: Autism, Pervasive Developmental Disorders, Eye Movements, Toddlers
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Cattell, Lindsay; Bruch, Julie – Regional Educational Laboratory Mid-Atlantic, 2021
This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students. Districts use early warning systems to target resources to the most at-risk students and intervene before students drop out. Schools want to ensure the early warning system accurately identifies…
Descriptors: At Risk Students, Identification, Artificial Intelligence, Dropout Prevention
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