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Okan Bulut; Tarid Wongvorachan; Surina He; Soo Lee – Discover Education, 2024
Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school…
Descriptors: High School Students, Dropouts, Identification, Man Machine Systems
Xia, Qi; Chiu, Thomas K. F.; Chai, Ching Sing – Education and Information Technologies, 2023
Artificial intelligence (AI) has the potential to support self-regulated learning (SRL) because of its strong anthropomorphic characteristics. However, most studies of AI in education have focused on cognitive outcomes in higher education, and little research has examined how psychological needs affect SRL with AI in the K-12 setting. SRL is a…
Descriptors: Artificial Intelligence, Grade 9, Student Needs, Gender Differences
Bizama, Marcela; Chávez-Castillo, Yasna – Electronic Journal of Research in Educational Psychology, 2023
Introduction: There is a variety of literature regarding reading development in its acquisitional stages, and also regarding reading disabilities that appear in early childhood. However, from the cognitive point of view, there has been less study of reading comprehension in secondary students. The principal aim of this study was to analyze the…
Descriptors: Foreign Countries, Secondary School Students, Reading Comprehension, Cognitive Processes
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
Yildiz, Muhammed Berke; Börekci, Caner – Journal of Educational Technology and Online Learning, 2020
Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data…
Descriptors: Grade Prediction, Academic Achievement, Artificial Intelligence, Grade 9
Psyridou, Maria; Tolvanen, Asko; Patel, Priyanka; Khanolainen, Daria; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Torppa, Minna – Scientific Studies of Reading, 2023
Purpose: We aim to identify the most accurate model for predicting adolescent (Grade 9) reading difficulties (RD) in reading fluency and reading comprehension using 17 kindergarten-age variables. Three models (neural networks, linear, and mixture) were compared based on their accuracy in predicting RD. We also examined whether the same or a…
Descriptors: Reading Difficulties, Networks, Artificial Intelligence, Predictor Variables
Hülür, Gizem; Gasimova, Fidan; Robitzsch, Alexander; Wilhelm, Oliver – Child Development, 2018
Intellectual engagement (IE) refers to enjoyment of intellectual activities and is proposed as causal for knowledge acquisition. The role of IE for cognitive development was examined utilizing 2-year longitudinal data from 112 ninth graders (average baseline age: 14.7 years). Higher baseline IE predicted higher baseline crystallized ability but…
Descriptors: Intellectual Experience, Learner Engagement, Cognitive Development, Longitudinal Studies
Treiman, Rebecca; Hulslander, Jacqueline; Olson, Richard K.; Willcutt, Erik G.; Byrne, Brian; Kessler, Brett – Scientific Studies of Reading, 2019
We examined the predictive value of early spelling for later reading performance by analyzing data from 970 U.S. children whose spelling was assessed in the summer following the completion of kindergarten (M age = 6 years; 3 months). The word reading performance of most of the children was then tested after the completion of Grade 1 (age 7;5),…
Descriptors: Reading Achievement, Spelling, Predictor Variables, Kindergarten
MacDonald, Amy; Carmichael, Colin – Mathematics Education Research Group of Australasia, 2016
International research suggests that early mathematical competences predicts later mathematical outcomes. In this paper, we build on our previous study of young children's mathematical competencies (MacDonald & Carmichael, 2015) to explore the relationship between mathematical competencies at 4-5 years, as measured by teacher ratings, and…
Descriptors: Foreign Countries, Longitudinal Studies, Mathematics Instruction, Young Children
Potocki, Anna; Sanchez, Monique; Ecalle, Jean; Magnan, Annie – Journal of Learning Disabilities, 2017
This article presents two studies investigating the role of executive functioning in written text comprehension in children and adolescents. In a first study, the involvement of executive functions in reading comprehension performance was examined in normally developing children in fifth grade. Two aspects of text comprehension were…
Descriptors: Executive Function, Children, Adolescents, Reading Difficulties
McElravy, L. J.; Hastings, Lindsay J. – Journal of Agricultural Education, 2014
The transfer of leadership to younger generations is an important factor in agricultural communities and is likely one reason developing leaders is a central mission of many youth organizations, including 4-H and FFA. In adults, researchers have extensively explored the relationship between personality traits and leadership (Judge, Bono, Ilies,…
Descriptors: Leadership Qualities, Personality Traits, Emotional Intelligence, Youth Programs
Murayama, Kou; Pekrun, Reinhard; Lichtenfeld, Stephanie; vom Hofe, Rudolf – Child Development, 2013
This research examined how motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (deep and surface strategies), and intelligence jointly predict long-term growth in students' mathematics achievement over 5 years. Using longitudinal data from six annual waves (Grades 5 through 10;…
Descriptors: Mathematics Achievement, Achievement Gains, Cognitive Processes, Learning Strategies
Cho, Seokhee; Lin, Chia-Yi – Roeper Review, 2011
Predictive relationships among perceived family processes, intrinsic and extrinsic motivation, incremental beliefs about intelligence, confidence in intelligence, and creative problem-solving practices in mathematics and science were examined. Participants were 733 scientifically talented Korean students in fourth through twelfth grades as well as…
Descriptors: Intelligence, Incentives, Talent, Motivation
Wright-Cunningham, Kamal Phillip – ProQuest LLC, 2012
Adolescence is a time when individuals are forced to deal with the stress of moving from being a teenager to an adult (Spear, 2000). As a result, this difficult developmental period is often characterized by adolescents engaging in risky behavior and potentially dangerous experimentation (Golden & Turner, 2005). The importance of understanding how…
Descriptors: At Risk Students, Special Education, Mental Health, Adolescents
Schick, Hella; Phillipson, Shane N. – High Ability Studies, 2009
In the development of performance excellence, the relative roles played by intellectual ability and motivation remain speculative. This study investigates the role played by general intelligence, school environment, self-efficacy, and aspects of personal identity in the formation of learning motivation in German students attending the Gymnasium…
Descriptors: Intelligence, Self Efficacy, Factor Structure, Learning Motivation
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