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Picones, Gio; PaaBen, Benjamin; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2022
In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…
Descriptors: Prediction, Academic Achievement, Learning Analytics, Concept Mapping
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Fitzpatrick, Kevin M.; Willis, Don – Journal of Early Adolescence, 2016
This article's aim is to examine correlates of middle school students' body mass index (BMI). Little research simultaneously has considered both child and parent correlates in predicting child's BMI; we examine the interrelationships between middle school students and their parent's risks and protective factors and their impact on the child's BMI.…
Descriptors: Risk, Body Composition, Middle School Students, Correlation
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Huang, Chi-Cheng – Information Research: An International Electronic Journal, 2017
Introduction: The ultimate viability of an information system is dependent on individuals' continued use of the information system. In this study, we use the technology acceptance model and the theory of interpersonal behaviour to predict continued use of information systems. Method: We established a Web questionnaire on the mySurvey Website and…
Descriptors: Information Systems, Technology Integration, Interpersonal Relationship, Prediction
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Cheema, Jehanzeb; Kitsantas, Anastasia – Educational Psychology, 2016
This study investigated the predictiveness of preferred learning styles (competitive and cooperative) and classroom climate (teacher support and disciplinary climate) on learning strategy use in mathematics. The student survey part of the Programme for International Student Assessment 2003 comprising of 4633 US observations was used in a weighted…
Descriptors: High School Students, Learning Strategies, Prediction, Preferences
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Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Reading Comprehension, Reading Achievement, Elementary School Students, Secondary School Students
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Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Response to Intervention, Achievement Gains, High Stakes Tests, Prediction