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Jyoti Prakash Meher; Rajib Mall – IEEE Transactions on Education, 2025
Contribution: This article suggests a novel method for diagnosing a learner's cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts…
Descriptors: Cognitive Ability, Assistive Technology, Adaptive Testing, Computer Assisted Testing
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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
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Yin Fei Chan; Jinghan Liang; Meng Xie; Xiaobo Shi; Dan Lin – Reading and Writing: An Interdisciplinary Journal, 2025
The current study illustrated a mediation model of different aspects of family environmental factors in predicting short-term memory and social understanding in Chinese children. The study investigated how children's short-term memory and social understanding were predicted by family environment factors, including socioeconomic status, home…
Descriptors: Family Environment, Short Term Memory, Social Development, Foreign Countries
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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
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Kun Sun; Rong Wang – Cognitive Science, 2025
The majority of research in computational psycholinguistics on sentence processing has focused on word-by-word incremental processing within sentences, rather than holistic sentence-level representations. This study introduces two novel computational approaches for quantifying sentence-level processing: sentence surprisal and sentence relevance.…
Descriptors: Reading Rate, Reading Comprehension, Sentences, Computation
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Halim Acosta; Seung Lee; Daeun Hong; Wookhee Min; Bradford Mott; Cindy Hmelo-Silver; James Lester – International Educational Data Mining Society, 2025
Understanding the relationship between student behaviors and learning outcomes is crucial for designing effective collaborative learning environments. However, collaborative learning analytics poses significant challenges, not only due to the complex interplay between collaborative problem-solving and collaborative dialogue but also due to the…
Descriptors: Learning Analytics, Cooperative Learning, Student Behavior, Prediction
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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
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Grace D. Jaiyeola; Aaron Y. Wong; Richard L. Bryck; Caitlin Mills; Stephen Hutt – International Educational Data Mining Society, 2025
This study explores the use of webcam-based eye tracking during a learning task to predict and better understand neurodivergence with the aim of improving personalized learning to support diverse learning needs. Using WebGazer, a webcam-based eye tracking technology, we collected gaze data from 354 participants as they engaged in educational…
Descriptors: Video Technology, Eye Movements, Neurodevelopmental Disorders, Artificial Intelligence
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Raditha Putri Cahyani; Adi Rahmat; Yanti Hamdiyati; A. Amprasto; Muhamad Wafda Jamil – Journal of Biological Education Indonesia (Jurnal Pendidikan Biologi Indonesia), 2025
The development of science and technology without individual environmental awareness has led to a decline in environmental quality. This issue can be addressed by enhancing environmental literacy, which includes ecological knowledge, cognitive skills, environmental attitudes, and behaviors. In practice, the question arises as to whether knowledge…
Descriptors: Foreign Countries, High School Students, Grade 11, Student Attitudes
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Yu Zhai; Yajing Xing; Jianlong Zhao; XiangYu He; Kexin Jiang; Tengfei Zhang; Chunming Lu – Journal of Speech, Language, and Hearing Research, 2025
Purpose: Children with congenital hearing loss (HL) have auditory impairments that may place them at increased risk for delays or variability in language development. However, obtaining reliable brain markers for early classification of young children with HL versus those with normal hearing (NH), as well as for precise assessment of HL children's…
Descriptors: Young Children, Hard of Hearing, Congenital Impairments, Mothers
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Mark A. Perkins; Jonathan W. Carrier; Joseph M. Schaffer – Community College Journal of Research and Practice, 2024
Community colleges often employ measures to determine student course placement. Though much research has examined the predictive validity of placement measures such as ACT or high-school GPA, little research examines the effects of students' traditional and non-traditional status. Using data from a rural state community college, we examined the…
Descriptors: Community College Students, Rural Schools, Nontraditional Students, Student Placement
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Selina L. P. Mushi – International Journal of Early Years Education, 2024
This research report is on fostering young children's metacognitive skills. The study was conducted at a private early childhood education center in a Midwestern city in the United States in 2020. The design of the study was a mixed approach including Time Series experimentation, naturalistic observation, and interviews. Children aged 3-4 years…
Descriptors: Metacognition, Preschool Education, Story Reading, Prediction
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Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
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Stephen Hunter; Carla Hilario; Karen A. Patte; Scott T. Leatherdale; Roman Pabayo – Journal of School Health, 2024
Background: Income inequality is theorized to impact health. However, evidence among adolescents is limited. This study examined the association between income inequality and health-related school absenteeism (HRSA) in adolescents. Methods: Participants were adolescents (n = 74,501) attending secondary schools (n = 136) that participated in the…
Descriptors: Correlation, Social Differences, Secondary School Students, Attendance
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Anthony S. DiStefano; Joshua S. Yang – Field Methods, 2024
Despite recent methodological advances in saturation, guidelines for its estimation in more complex research designs--such as ethnographic studies--have been lacking. We present an accessible, step-by-step approach to empirical assessment of data saturation, tested on a moderately sized ethnographic study with 109 combined direct observations and…
Descriptors: Sample Size, Ethnography, Research Methodology, Research Design
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