tandfonline.com – Model-Based Clustering and Classification for Data Science: With Applications in R
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tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: Link til kilde
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tandfonline.com – Using model-based scaffolds to support students solving context-based chemistry problems
tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: ABSTRACT ABSTRACT Context-based learning aims to make learning more meaningful by raising meaningful problems. However, these types of problems often require reflection and thinking processes that are more complex and thus more difficult for students, putting high demands on students’ problem-solving capabilities. In this paper, students’ approaches when solving context-based chemistry problems and effects of systematic scaffolds are analysed based on the Model of Hierarchical Complexity. Most answers were initially assigned to the lowest level of the model; higher levels were reached without scaffolds only by few students and by most students with scaffolds. The results are discussed with regard to practical implications in terms of how teachers could make use of context-based tasks and aligned scaffolds to help students… Continue Reading
tandfonline.com – Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches
tandfonline.com har udgivet en rapport under søgningen “Teacher Education Mathematics”: ABSTRACT ABSTRACT Two classes of methods properly account for clustering of data: design-based methods and model-based methods. Estimates from both methods have been shown to be approximately equal with large samples. However, both classes are known to produce biased standard error estimates with small samples. This paper compares the bias of standard errors and statistical power of marginal effects for generalized estimating equations (a design-based method) and generalized/linear mixed effects models (model-based methods) with small sample sizes via a simulation study. Provided that the distributional assumptions are met, model-based methods produced the least-biased standard error estimates and greater relative statistical power. Link til kilde