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Showing 1 to 15 of 26 results Save | Export
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Mizumoto, Atsushi – Language Learning, 2023
Researchers often make claims regarding the importance of predictor variables in multiple regression analysis by comparing standardized regression coefficients (standardized beta coefficients). This practice has been criticized as a misuse of multiple regression analysis. As a remedy, I highlight the use of dominance analysis and random forests, a…
Descriptors: Predictor Variables, Artificial Intelligence, Evaluation Methods, Multiple Regression Analysis
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Abdessamad Chanaa; Nour-eddine El Faddouli – Journal of Education and Learning (EduLearn), 2024
Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning…
Descriptors: Electronic Learning, Evaluation Methods, Artificial Intelligence, Taxonomy
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Wenyi Lu; Joseph Griffin; Troy D. Sadler; James Laffey; Sean P. Goggins – Journal of Learning Analytics, 2025
Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…
Descriptors: Game Based Learning, Predictor Variables, Evaluation Methods, Educational Games
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Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
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Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
Chiu, Y. D. – Remedial and Special Education, 2018
We assessed the simple view of reading as a framework for Grade 3 reading comprehension in two ways. We first confirmed that a structural equation model in which word recognition, listening comprehension, and reading comprehension were assessed by multiple measures to inform each latent construct provided an adequate fit to this model in Grade 3.…
Descriptors: Reading Instruction, Reading Comprehension, Grade 3, Word Recognition
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Aiello, Rachel; Ruble, Lisa; Esler, Amy – Journal of Applied School Psychology, 2017
This study aimed to better understand predictors of evidence-based assessment practices for autism spectrum disorder (ASD). Nationwide, 402 school psychologists were surveyed for their knowledge of and training and experience with ASD on assessment practices, including reported areas of training needs. The majority of school psychologists reported…
Descriptors: School Psychologists, National Surveys, Evidence Based Practice, Pervasive Developmental Disorders
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Seethaler, Pamela M.; Fuchs, Lynn S.; Fuchs, Douglas; Compton, Donald L. – Grantee Submission, 2016
The purpose of this study was to assess the added value of dynamic assessment (DA) beyond more conventional static measures for predicting individual differences in year-end 1st-grade calculation (CA) and word-problem (WP) performance, as a function of limited English proficiency (LEP) status. At the start of 1st grade, students (129 LEP; 163…
Descriptors: Grade 1, Elementary School Students, Limited English Speaking, Mathematics Instruction
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Sorjonen, Kimmo; Hemmingsson, Tomas; Lundin, Andreas; Falkstedt, Daniel; Melin, Bo – Intelligence, 2012
The question whether a person's attained socioeconomic position is mainly due to hers/his intelligence, socioeconomic background, or level of education, has sparked some controversy. In the present study, the effects of these three variables, as well as emotional capacity, on attained occupational position and on income were analyzed with…
Descriptors: Intelligence, Income, Structural Equation Models, Academic Achievement
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Kanne, Stephen M.; Gerber, Andrew J.; Quirmbach, Linda M.; Sparrow, Sara S.; Cicchetti, Domenic V.; Saulnier, Celine A. – Journal of Autism and Developmental Disorders, 2011
The relationship between adaptive functioning and autism symptomatology was examined in 1,089 verbal youths with ASD examining results on Vineland-II, IQ, and measures of ASD severity. Strong positive relationships were found between Vineland subscales and IQ. Vineland Composite was negatively associated with age. IQ accounted a significant amount…
Descriptors: Autism, Intelligence Quotient, Adjustment (to Environment), Severity (of Disability)
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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
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Coyle, Thomas R.; Pillow, David R. – Intelligence, 2008
This research examined whether the SAT and ACT would predict college grade point average (GPA) after removing g from the tests. SAT and ACT scores and freshman GPAs were obtained from a university sample (N=161) and the 1997 National Longitudinal Study of Youth (N=8984). Structural equation modeling was used to examine relationships among g, GPA,…
Descriptors: Intelligence, Grade Point Average, Structural Equation Models, Predictive Validity
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Stuebing, Karla K.; Barth, Amy E.; Molfese, Peter J.; Weiss, Brandon; Fletcher, Jack M. – Exceptional Children, 2009
A meta-analysis of 22 studies evaluating the relation of different assessments of IQ and intervention response did not support the hypothesis that IQ is an important predictor of response to instruction. We found an R[superscript 2] of 0.03 in models with IQ and the autoregressor as predictors and a unique lower estimated R[superscript 2] of 0.006…
Descriptors: Intervention, Intelligence Quotient, Effect Size, Reading Instruction
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Kidd, Evan; Lum, Jarrad A. G. – Developmental Science, 2008
Hartshorne and Ullman (2006 ) presented naturalistic language data from 25 children (15 boys, 10 girls) and showed that girls produced more past tense overregularization errors than did boys. In particular, girls were more likely to overregularize irregular verbs whose stems share phonological similarities with regular verbs. It was argued that…
Descriptors: Females, Verbs, Gender Differences, Males
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Kyllonen, Patrick C.; Walters, Alyssa M.; Kaufman, James C. – ETS Research Report Series, 2011
This report reviews the literature on noncognitive and other background predictors (e.g., personality, attitudes, and interests) as it pertains to graduate education. The first section reviews measures typically used in studies of graduate school outcomes, such as attrition and time to degree. A review of qualities faculty members and…
Descriptors: Predictor Variables, Student Characteristics, Graduate Students, Graduate Study
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