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Tan, Teck Kiang – Practical Assessment, Research & Evaluation, 2023
Researchers often have hypotheses concerning the state of affairs in the population from which they sampled their data to compare group means. The classical frequentist approach provides one way of carrying out hypothesis testing using ANOVA to state the null hypothesis that there is no difference in the means and proceed with multiple comparisons…
Descriptors: Comparative Analysis, Hypothesis Testing, Statistical Analysis, Guidelines
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Van Lissa, Caspar J.; van Erp, Sara; Clapper, Eli-Boaz – Research Synthesis Methods, 2023
When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…
Descriptors: Bayesian Statistics, Regression (Statistics), Maximum Likelihood Statistics, Meta Analysis
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Melissa G. Wolf; Daniel McNeish – Grantee Submission, 2023
To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to…
Descriptors: Programming Languages, Cutting Scores, Benchmarking, Factor Analysis
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Szlávi,Péter; Zsakó, László – Acta Didactica Napocensia, 2017
As a programmer when solving a problem, a number of conscious and unconscious cognitive operations are being performed. Problem-solving is a gradual and cyclic activity; as the mind is adjusting the problem to its schemas formed by its previous experiences, the programmer gets closer and closer to understanding and defining the problem. The…
Descriptors: Problem Solving, Programming, Mathematics, Programming Languages
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Mirolo, Claudio; Izu, Cruz; Lonati, Violetta; Scapin, Emanuele – Informatics in Education, 2021
When we "think like a computer scientist," we are able to systematically solve problems in different fields, create software applications that support various needs, and design artefacts that model complex systems. Abstraction is a soft skill embedded in all those endeavours, being a main cornerstone of computational thinking. Our…
Descriptors: Computer Science Education, Soft Skills, Thinking Skills, Abstract Reasoning
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Becker, Brett A.; Glanville, Graham; Iwashima, Ricardo; McDonnell, Claire; Goslin, Kyle; Mooney, Catherine – Computer Science Education, 2016
Programming is an essential skill that many computing students are expected to master. However, programming can be difficult to learn. Successfully interpreting compiler error messages (CEMs) is crucial for correcting errors and progressing toward success in programming. Yet these messages are often difficult to understand and pose a barrier to…
Descriptors: Computer Science Education, Programming, Novices, Error Patterns
Goldenson, Dennis – 1996
The assertion that "higher order" thinking skills can be improved by learning to program computers is not a new one. The idea endures even though the empirical evidence over the years has been mixed at best. In fact, there is no reason to expect that all programming courses will have identical, or even similar, effects. Such courses typically…
Descriptors: Academic Achievement, Authoring Aids (Programming), Computer Software, Computers