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Matthew D. Blanchard; Eugene Aidman; Lazar Stankov; Sabina Kleitman – Cognitive Research: Principles and Implications, 2025
A collective intelligence factor (CI) was introduced by prior research to characterise the cognitive ability of groups. Surprisingly, individual intelligence did not predict CI. Instead, it correlated with individual social sensitivity, the equality of conversational turn-taking, and the proportion of females in a group. However, these findings…
Descriptors: Intelligence, Cooperative Learning, Participative Decision Making, Metacognition
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Sinead Rhodes; Josephine N. Booth; Emily McDougal; Jessica Oldridge; Karim Rivera-Lares; Alexia Revueltas Roux; Tracy M. Stewart – Journal of Autism and Developmental Disorders, 2025
We examined whether cognitive profiles or diagnostic outcomes are better predictors of literacy performance for children being considered for an ADHD diagnosis. Fifty-five drug naïve children (M[subscript age] = 103.13 months, SD = 18.65; 29.09% girls) were recruited from an ADHD clinical referral waiting list. Children underwent assessment of IQ,…
Descriptors: Executive Function, Predictor Variables, Profiles, Literacy
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Lucy Shiels; Peter Carew; Dani Tomlin; Gary Rance – npj Science of Learning, 2025
This study investigated the impact of soundfield amplification (SFA) on reading fluency in normal-hearing students (n = 84) aged 8-10 years. Twenty-three grade 3 and 4 classes participated across three academic terms, alternating between SFA-On and SFA-Off conditions. Reading fluency was assessed using the Wheldall Assessment of Reading Passages.…
Descriptors: Classroom Environment, Acoustics, Reading Fluency, Hearing (Physiology)
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Rianne Suelmann; Eric Blaauw – Journal of Intellectual & Developmental Disability, 2025
Background: Addiction medicine still largely neglects the topic of mild and borderline intellectual disabilities (MBID), although patients with MBID are considered a risk group for substance-related problems and offending behaviour. This study aimed to explore the cognitive and adaptive impairments of inpatients in forensic addiction mental health…
Descriptors: Substance Abuse, Mild Intellectual Disability, At Risk Persons, Mental Health
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Igor Esnaola; Sara Martínez-Gregorio; Lorea Azpiazu; Iratxe Antonio-Agirre; Amparo Oliver – Psychology in the Schools, 2025
The main goal of this study was to analyze a longitudinal model which reviews the relationships between parent trust, trait emotional intelligence (EI) and self-concept. The sample was composed of 484 Spanish adolescents (226 boys, 258 girls) who completed the questionnaires "Parent Trust and Understanding Scale, Emotional Quotient Inventory:…
Descriptors: Parent Attitudes, Trust (Psychology), Emotional Intelligence, Self Concept
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Michael Generalo Albino; Femia Solomon Albino; John Mark R. Asio; Ediric D. Gadia – International Journal of Technology in Education, 2025
Technology has contributed so much to the development and innovation of humankind. Artificial Intelligence (AI) is an off-shoot of such. This article explored the influence of AI anxiety on AI self-efficacy among college students. The investigators used a cross-sectional research design for 695 purposively chosen college students in one higher…
Descriptors: Anxiety, Artificial Intelligence, Self Efficacy, College Students
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Haowen Zheng; Siwei Cheng – Sociological Methods & Research, 2025
How well can individuals' parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply,…
Descriptors: Socioeconomic Status, Adults, Parent Background, Social Stratification
Michael L. Chrzan; Francis A. Pearman; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…
Descriptors: Artificial Intelligence, Electronic Learning, School Districts, School Closing
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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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Valentine Joseph Owan; Ibrahim Abba Mohammed; Ahmed Bello; Tajudeen Ahmed Shittu – Contemporary Educational Technology, 2025
Despite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology…
Descriptors: Student Behavior, Foreign Countries, Artificial Intelligence, Natural Language Processing
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Yoon Lee; Gosia Migut; Marcus Specht – British Journal of Educational Technology, 2025
Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI…
Descriptors: Artificial Intelligence, Cognitive Processes, Student Behavior, Cues
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Sarab Tej Singh; Satish Kumar; Vishal Singh – Journal of Education and Learning (EduLearn), 2025
The current research is the study of academic buoyancy in relation to emotional intelligence and parenting styles. Academic buoyancy is a strength in a student's life to deal with the routine problems in classroom study like low grades, negative feedback by teachers, and difficulties in understanding of concepts. For the studying the relationship…
Descriptors: Parenting Styles, Emotional Intelligence, Predictor Variables, Academic Achievement
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Yuntian Xie; Ying Li; Taowen Yu; Yuxuan Liu – Education and Information Technologies, 2025
This study aimed to develop and validate the Metacognitions about Generative AI Use Scale (MGAUS) to assess college students' metacognitive beliefs about generative AI and to explore these metacognitions as predictors of generative AI addiction risk. A total of 1229 college students from China participated in the study, providing data through an…
Descriptors: Foreign Countries, College Students, Metacognition, Student Attitudes
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Juan Andrés Talamás-Carvajal; Héctor G. Ceballos; Isabel Hilliger – Journal of Learning Analytics, 2025
Artificial intelligence (AI) is currently leading an industrial revolution in most aspects of human life, and education is no exception. With the increasing ratio of students to faculty, AI could be an extremely beneficial tool for individual mentoring; for example, for cases of dropout and for student retention. While many models have already…
Descriptors: Higher Education, Artificial Intelligence, Research Methodology, Student Subcultures
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Samantha M. van Rens; Cristina Lemelin; Patricia H. Kloosterman; Laura J. Summerfeldt; James D. A. Parker – Canadian Journal of School Psychology, 2025
Although previous research has found trait emotional intelligence (TEI) to be a moderate predictor of bullying behaviors in adolescents, this work has limited generalizability. The current study is the first to use a multidimensional approach to both TEI and bullying behaviors when looking at their relationship in high school students. The study…
Descriptors: Bullying, High School Students, Emotional Intelligence, Predictor Variables
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