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Rochdi Boudjehem; Yacine Lafifi – Education and Information Technologies, 2024
Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue…
Descriptors: At Risk Students, Identification, Dropouts, Student Behavior
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Mauricio Garnier-Villarreal; Terrence D. Jorgensen – Grantee Submission, 2024
Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are use to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Indexes
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Simon Šuster; Timothy Baldwin; Karin Verspoor – Research Synthesis Methods, 2024
Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of available training data. In this study, we investigate the effectiveness of generative large language…
Descriptors: Medical Research, Safety, Experimental Groups, Control Groups
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Céline Hidalgo; Christelle Zielinski; Sophie Chen; Stéphane Roman; Eric Truy; Daniele Schön – International Journal of Language & Communication Disorders, 2024
Background: Perceptual and speech production abilities of children with cochlear implants (CIs) are usually tested by word and sentence repetition or naming tests. However, these tests are quite far apart from daily life linguistic contexts. Aim: Here, we describe a way of investigating the link between language comprehension and anticipatory…
Descriptors: Deafness, Assistive Technology, Language Skills, Verbal Communication
Michael Wade Ashby – ProQuest LLC, 2024
Whether machine learning algorithms effectively predict college students' course outcomes using learning management system data is unknown. Identifying students who will have a poor outcome can help institutions plan future budgets and allocate resources to create interventions for underachieving students. Therefore, knowing the effectiveness of…
Descriptors: Artificial Intelligence, Algorithms, Prediction, Learning Management Systems
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Michelle Menezes; Jim Soland; Micah O. Mazurek – Journal of Autism and Developmental Disorders, 2024
The capacity of families with autistic children to demonstrate resilience is a notable strength that has received little attention in the literature. A potential predictor of family resilience in households with autistic youth is neighborhood support. This study examined the relationship between neighborhood support and family resilience in…
Descriptors: Family Environment, Resilience (Psychology), Neighborhoods, Social Support Groups
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Manon D. Gouiran; Florian Cova – Cognitive Science, 2024
Past research on people's moral judgments about moral dilemmas has revealed a connection between utilitarian judgment and reflective cognitive style. This has traditionally been interpreted as reflection is conducive to utilitarianism. However, recent research shows that the connection between reflective cognitive style and utilitarian judgments…
Descriptors: Moral Values, Cognitive Style, Prosocial Behavior, Decision Making
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Ulrike Padó; Yunus Eryilmaz; Larissa Kirschner – International Journal of Artificial Intelligence in Education, 2024
Short-Answer Grading (SAG) is a time-consuming task for teachers that automated SAG models have long promised to make easier. However, there are three challenges for their broad-scale adoption: A technical challenge regarding the need for high-quality models, which is exacerbated for languages with fewer resources than English; a usability…
Descriptors: Grading, Automation, Test Format, Computer Assisted Testing
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Kylie Anglin – AERA Open, 2024
Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a…
Descriptors: Validity, Artificial Intelligence, Models, Best Practices
Emily J. Barnes – ProQuest LLC, 2024
This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…
Descriptors: Artificial Intelligence, Higher Education, Models, Prediction
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Milos Ilic; Goran Kekovic; Vladimir Mikic; Katerina Mangaroska; Lazar Kopanja; Boban Vesin – IEEE Transactions on Learning Technologies, 2024
In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…
Descriptors: Artificial Intelligence, Academic Achievement, Prediction, Programming
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Helia M. Aval; Kasey Pankratz; Elizabeth L. Davis – Merrill-Palmer Quarterly: A Peer Relations Journal, 2024
Children's responses to new, unfamiliar social interactions should be influenced by their cognitive appraisals and physiology, though little is known about how these constructs interrelate. To investigate these links, we examined whether children's appraisals of recalled events and resting parasympathetic physiology predicted social…
Descriptors: Recall (Psychology), Physiology, Problem Solving, Child Behavior
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Lief Esbenshade; Jonathan Vitale; Ryan S. Baker – International Educational Data Mining Society, 2024
In a number of settings risk prediction models are being used to predict distal future outcomes for individuals, including high school risk prediction. We propose a new method, non-overlapping-leave-future-out (NOLFO) validation, to be used in settings with long delays between feature and outcome observation and where there are overlapping…
Descriptors: Risk, Prediction, Models, High School Students
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Scott Crossley; Yu Tian; Joon Suh Choi; Langdon Holmes; Wesley Morris – International Educational Data Mining Society, 2024
This study examines the potential to use keystroke logs to examine differences between authentic writing and transcribed essay writing. Transcribed writing produced within writing platforms where copy and paste functions are disabled indicates that students are likely copying texts from the internet or from generative artificial intelligence (AI)…
Descriptors: Plagiarism, Writing (Composition), Essays, Artificial Intelligence
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Ren, Ping; Yang, Liu; Luo, Fang – Education and Information Technologies, 2023
Student feedback is crucial for evaluating the performance of teachers and the quality of teaching. Free-form text comments obtained from open-ended questions are seldom analyzed comprehensively since it is difficult to interpret and score compared to standardized rating scales. To solve this problem, the present study employed aspect-level…
Descriptors: Student Attitudes, Student Evaluation of Teacher Performance, Feedback (Response), Prediction
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