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Canivez, Gary L.; Youngstrom, Eric A. – Applied Measurement in Education, 2019
The Cattell-Horn-Carroll (CHC) taxonomy of cognitive abilities married John Horn and Raymond Cattell's Extended Gf-Gc theory with John Carroll's Three-Stratum Theory. While there are some similarities in arrangements or classifications of tasks (observed variables) within similar broad or narrow dimensions, other salient theoretical features and…
Descriptors: Taxonomy, Cognitive Ability, Intelligence, Cognitive Tests
Canivez, Gary L.; Watkins, Marley W.; McGill, Ryan J. – British Journal of Educational Psychology, 2019
Background: There is inadequate information regarding the factor structure of the Wechsler Intelligence Scale for Children -- Fifth UK Edition (WISC-V[superscript UK]; Wechsler, 2016a, Wechsler Intelligence Scale for Children-Fifth UK Edition, Harcourt Assessment, London, UK) to guide interpretation. Aims and methods: The WISC-V[superscript UK]…
Descriptors: Children, Intelligence Tests, Construct Validity, Factor Analysis
OECD Publishing, 2021
Artificial intelligence (AI) and robotics are major breakthrough technologies that are transforming the economy and society. The OECD's Artificial Intelligence and the Future of Skills (AIFS) project is developing a programme to assess the capabilities of AI and robotics, and their impact on education and work. This volume reports on the first…
Descriptors: Artificial Intelligence, Skill Development, Evaluation, Competence
Dorfman, Leonid; Kalugin, Alexey; Mishkevich, Arina – International Society for Technology, Education, and Science, 2021
The commonality is one of underlying conditions that provide the individual-intellectual integrations. Three forms identify the commonality. The first is the causal commonality, the second is the generalizing commonality, third is the intertwining commonality. Confirmatory one- and two- factor analysis (CFA) and path analysis (PA) specified the…
Descriptors: Undergraduate Students, Factor Analysis, Individual Characteristics, Attribution Theory
Dale, Brittany A.; Finch, W. Holmes; Shellabarger, Kassie A. R.; Davis, Andrew – Journal of Psychoeducational Assessment, 2021
The Wechsler Intelligence Scales for Children (WISC) are the most widely used instrument in assessing cognitive ability, especially with children with autism spectrum disorder (ASD). Previous literature on the WISC has demonstrated a divergent pattern of performance on the WISC for children ASD compared to their typically developing peers;…
Descriptors: Children, Intelligence Tests, Profiles, Autism
Stevenson, Claire; Baas, Matthijs; van der Maas, Han – Journal of Intelligence, 2021
Despite decades of extensive research on creativity, the field still combats psychometric problems when measuring individual differences in creative ability and people's potential to achieve real-world outcomes that are both original and useful. We think these seemingly technical issues have a conceptual origin. We therefore propose a minimal…
Descriptors: Creativity, Psychometrics, Individual Differences, Theories
Wang, Tengfei; Li, Chenyu; Ren, Xuezhu; Schweizer, Karl – Journal of Intelligence, 2021
Working memory capacity (WMC) and fluid intelligence (Gf) are highly correlated, but what accounts for this relationship remains elusive. Process-overlap theory (POT) proposes that the positive manifold is mainly caused by the overlap of domain-general executive processes which are involved in a battery of mental tests. Thus, executive processes…
Descriptors: Short Term Memory, Executive Function, Intelligence, Foreign Countries
Lincke, Alisa; Jansen, Marc; Milrad, Marcelo; Berge, Elias – Research and Practice in Technology Enhanced Learning, 2021
Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners' interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the…
Descriptors: Artificial Intelligence, Prediction, Electronic Learning, Repetition
Fernandez, Jose M.; Yetter, Erin A.; Holder, Kim – Journal of Economic Education, 2021
The authors of this article use text mining techniques to uncover hidden or latent topics in economic education. The common use of JEL codes only identifies the academic setting for each paper but does not identify the underlying economic concept the paper addresses. An unsupervised machine learning algorithm called Latent Dirichlet Allocation is…
Descriptors: Economics Education, Educational Research, Artificial Intelligence, Scholarship
Bertolini, Roberto; Finch, Stephen J.; Nehm, Ross H. – Journal of Science Education and Technology, 2021
High levels of attrition characterize undergraduate science courses in the USA. Predictive analytics research seeks to build models that identify at-risk students and suggest interventions that enhance student success. This study examines whether incorporating a novel assessment type (concept inventories [CI]) and using machine learning (ML)…
Descriptors: Evaluation Methods, Scores, Artificial Intelligence, Grade Prediction
Farhan, Fikri; Rofi'ulmuiz, M. Abdul – International Journal of Evaluation and Research in Education, 2021
Learning achievement was one of the indicators often used to measure student success in learning. A comprehensive understanding of this topic requires contributions from a variety of disciplines. Recently, researchers are interested in examining the impact of religiosity and emotional intelligence on learning achievement. However, the study on…
Descriptors: Religious Factors, Emotional Intelligence, Islam, Academic Achievement
Ryan, Joseph J.; Glass Umfleet, Laura; Gontkovsky, Samuel T. – Journal of Psychoeducational Assessment, 2021
This investigation provides internal consistency reliabilities for the Wechsler Memory Scale--Fourth Edition (WMS-IV) subtest and index discrepancy scores using the standardization samples of the Adult and Older Adult batteries. Subtest reliabilities ranged from 0.00 to 0.93 for Adults and 0.25 to 0.94 for Older Adults. Three of 91 Adult…
Descriptors: Cognitive Tests, Memory, Adults, Intelligence Tests
Kovalkov, Anastasia; Paaßen, Benjamin; Segal, Avi; Pinkwart, Niels; Gal, Kobi – IEEE Transactions on Learning Technologies, 2021
Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this article, we make the journey from defining a formal measure of creativity, that is, efficiently computable to applying the measure in a practical domain. The measure is general and relies on core theoretical concepts in…
Descriptors: Creativity, Programming, Measurement Techniques, Models
Webb, Mary E.; Fluck, Andrew; Magenheim, Johannes; Malyn-Smith, Joyce; Waters, Juliet; Deschênes, Michelle; Zagami, Jason – Educational Technology Research and Development, 2021
Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in…
Descriptors: Artificial Intelligence, Learning, Adjustment (to Environment), Accountability
Lwande, Charles; Oboko, Robert; Muchemi, Lawrence – Education and Information Technologies, 2021
Learning Management Systems (LMS) lack automated intelligent components that analyze data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaires related to a specific learning style model and cognitive psychometric tests have been used to identify such behavior. The problem with…
Descriptors: Integrated Learning Systems, Student Behavior, Prediction, Artificial Intelligence

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