Unraveling the relation between representational competence and conceptual knowledge across four samples from two different countries

Abstract

IntroductionWhereas it is commonly assumed that in learning science, representational competence is a critical prerequisite for the acquisition of conceptual knowledge, comprehensive psychometric investigations of this assumption are rare. We undertake a step in this direction by re-analyzing the data from a recent study that found a substantial correlation between the two constructs in undergraduates in the context of field representations and electromagnetism.MethodsWe re-analyze the data (N = 515 undergraduate students; Mage = 21.81, SDage = 4.04) to examine whether the relation between representational competence and conceptual knowledge, both measured with psychometrically validated test instruments, is similar or varies between four samples from two countries. To this end, we will employ correlational analysis and scatter plots. Employing these methods, we will examine whether a positive relation between representational competence and conceptual knowledge can be found and is of similar magnitude in all samples. We will also employ multiple-group latent profile analysis to examine how the more detailed association between the two constructs varies or is similar across samples. Finally, we will examine how commonalities and differences between samples relate to aspects of learners’ gender, topic-specific learning opportunities, and individual preferences for specific learning content. The aim is to unravel the generalizability of this relation and thereby derive hypotheses for potential moderating factors that can be further examined in future research.

The relation of representational competence and conceptual knowledge in female and male undergraduates

“…Note that although we are not interested in examining similarities and differences in the relation between the constructs across the different sub-samples that were used in this study, we will correct standard errors and confidence intervals for all statistical estimates, including estimates of correlations and Cohen’s d s, for cluster dependence via cluster-robust maximum likelihood estimation (Szpiro et al, 2010 ). Comparative analyses across the four samples are presented elsewhere (Edelsbrunner & Hofer, 2023 ) and the data for further comparisons are freely available (Malone et al, 2021 ). Standard errors and confidence intervals will also be corrected for deviation from bivariate normality with a multivariate kurtosis-robust estimator (Yuan et al, 2004 ).…”