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Matsumoto, David; Hwang, Hyisung C. – Applied Cognitive Psychology, 2021
Research on investigative interviewing has highlighted the role of rapport in nonconfrontational, evidence-based interviewing procedures, but questions remain about the nature and function of rapport in such interviews. Across three samples drawn from multiple previous studies involving similar methodologies, we addressed four issues: a potential…
Descriptors: Interpersonal Communication, Interpersonal Relationship, Interviews, Prediction
Hongli Yang; Jingjing Xiang – Psychology in the Schools, 2025
The relationship between peer support and academic engagement has been widely explored. However, scarce research examined this relationship for college students, and little was known about the underlying mechanism under this relationship. This study aimed to examine the effect of peer support on academic via the mediation of academic motivation…
Descriptors: Peer Relationship, Academic Achievement, Student Motivation, Measurement
Helen Hendry; Eleonora Teszenyi; Lucy Rodriguez-Leon; Mary-Louise Maynes; Jane Dorrian; Tracey Edwards – European Early Childhood Education Research Journal, 2025
Research in early childhood settings requires careful consideration of the impact on all children in the setting, whether participants or non-participants, and evolving ethical approaches in response to children's needs. However, flexible approaches and, 'in the moment', ethical adaptations are not routinely reported as part of early childhood…
Descriptors: Ethics, Prediction, Educational Research, Early Childhood Education
David Wilkins; Melissa Meindl – Child Care in Practice, 2025
Across the UK, child protection social workers are routinely called upon to assess the likelihood of future significant harm to children. Yet making consistently accurate judgements about what may or may not happen in future can be a difficult task. In a previous study, we tested social workers' abilities (n = 283) to forecast the likelihood of…
Descriptors: Caseworkers, Social Work, Prediction, Futures (of Society)
Shan Li; Xiaoshan Huang; Tingting Wang; Juan Zheng; Susanne P. Lajoie – Journal of Computing in Higher Education, 2025
Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students' reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed…
Descriptors: Information Retrieval, Artificial Intelligence, Prediction, Abstract Reasoning
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
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
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
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
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
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
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
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
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
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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