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Showing 1 to 15 of 17 results Save | Export
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Joel S. Steele; Kevin J. Grimm – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax…
Descriptors: Structural Equation Models, Programming, Orthographic Symbols, Syntax
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Suppanut Sriutaisuk; Yu Liu; Seungwon Chung; Hanjoe Kim; Fei Gu – Educational and Psychological Measurement, 2025
The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two…
Descriptors: Structural Equation Models, Error of Measurement, Programming Languages, Goodness of Fit
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Vispoel, Walter P.; Lee, Hyeryung; Xu, Guanlan; Hong, Hyeri – Journal of Experimental Education, 2023
Although generalizability theory (GT) designs have traditionally been analyzed within an ANOVA framework, identical results can be obtained with structural equation models (SEMs) but extended to represent multiple sources of both systematic and measurement error variance, include estimation methods less likely to produce negative variance…
Descriptors: Generalizability Theory, Structural Equation Models, Programming Languages, Scores
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Manuel T. Rein; Jeroen K. Vermunt; Kim De Roover; Leonie V. D. E. Vogelsmeier – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive…
Descriptors: Measurement Techniques, Factor Analysis, Scores, Validity
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Aykut Durak; Vahide Bulut – Technology, Knowledge and Learning, 2025
The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the…
Descriptors: Prediction, Low Achievement, High Achievement, Academic Achievement
Yujiao Mai; Ziqian Xu; Zhiyong Zhang; Ke-Hai Yuan – Grantee Submission, 2023
Structural equation modeling (SEM) is widely used in behavioral, social, and education research. Drawing publication-ready path diagrams for SEM is not a pleasant task with the existing software. The article introduces an open-source web-based graphical application, "semdiag," for drawing WYSIWYG SEM path diagrams interactively. The…
Descriptors: Open Source Technology, Web 2.0 Technologies, Freehand Drawing, Path Analysis
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Fowler, Max; Smith, David H., IV; Hassan, Mohammed; Poulsen, Seth; West, Matthew; Zilles, Craig – Computer Science Education, 2022
Background and Context: Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective: This study aims to replicate a…
Descriptors: Programming, Computer Science Education, Correlation, Introductory Courses
Merkle, Edgar C.; Fitzsimmons, Ellen; Uanhoro, James; Goodrich, Ben – Grantee Submission, 2021
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…
Descriptors: Bayesian Statistics, Structural Equation Models, Psychometrics, Factor Analysis
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Silvia Wen-Yu Lee; Jyh-Chong Liang; Chung-Yuan Hsu; Meng-Jung Tsai – Interactive Learning Environments, 2024
While research has shown that students' epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students' epistemic beliefs…
Descriptors: Student Attitudes, Beliefs, Computer Science Education, Programming
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Yildiz Durak, Hatice – Journal of Computer Assisted Learning, 2018
The aim of this study is to investigate the effect of students' flipped learning readiness (FLR) on engagement, programming self-efficacy, attitude towards programming, and interaction intensity in the information and technology classrooms where programming is taught with the flipped classroom (FC) model. The study group of this research, which is…
Descriptors: Teaching Methods, Middle School Students, Blended Learning, Self Efficacy
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Kuo, Feng-Yang; Wu, Wen-Hsiung; Lin, Cathy S. – Journal of Educational Computing Research, 2013
Today, information technology (IT) has permeated virtually every aspect of our society and the learning of software programming is becoming increasingly important to the creation and maintenance of the IT infrastructure critical to our daily life. In this article, we report the results of a study that demonstrates how the self-regulation paradigm…
Descriptors: Computer Science Education, Programming, College Students, Programming Languages
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Hsu, Ting-Chia; Hwang, Gwo-Jen – Educational Technology & Society, 2017
Programming concepts are important and challenging to novices who are beginning to study computer programming skills. In addition to the textbook content, students usually learn the concepts of programming from the web; however, it could be difficult for novice learners to effectively derive helpful information from such non-structured open…
Descriptors: Web Sites, Teaching Methods, Computer Science Education, Information Sources
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Al-Azawei, Ahmed; Al-Bermani, Ali; Lundqvist, Karsten – Journal of Information Technology Education: Research, 2016
This study investigated the complex relationship among learning styles, gender, perceived satisfaction, and academic performance across four programming courses supported by an e-learning platform. A total of 219 undergraduate students from a public Iraqi university who recently experienced e-learning voluntarily took place in the study. The…
Descriptors: Cognitive Style, Programming, Foreign Countries, Undergraduate Students
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Finch, W. Holmes; Bronk, Kendall Cotton – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Latent class analysis (LCA) is an increasingly popular tool that researchers can use to identify latent groups in the population underlying a sample of responses to categorical observed variables. LCA is most commonly used in an exploratory fashion whereby no parameters are specified a priori. Although this exploratory approach is reasonable when…
Descriptors: Structural Equation Models, Computer Software, Programming, Goodness of Fit
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Andrews, D. G. H. – Physics Education, 2009
A computer model of the decay of a radioactive series, written in Visual Basic in Excel[TM], is presented. The model is based on the random selection of cells in an array. The results compare well with the theoretical equations. The model is a useful tool in teaching this aspect of radioactivity. (Contains 4 figures.)
Descriptors: Radiation, Structural Equation Models, Programming, Mathematical Models
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