The Relation of Representational Competence and Conceptual Knowledge in Female and Male Undergraduates

Abstract

BackgroundRepresentational competence is commonly considered to be a prerequisite for the acquisition of conceptual knowledge, yet little exploration has been undertaken into the relation between these two constructs. Using an assessment of representational competence with vector fields that functions without confounding topical context, we examined its relation with N = 515 undergraduates’ conceptual knowledge about electromagnetism.ResultsApplying latent variable modeling, we found that students’ representational competence and conceptual knowledge are related yet clearly distinguishable constructs (r = .71). The relation was weaker for female than for male students, which could not be explained by measurement differences between the two groups. There were several students with high representational competence and low conceptual knowledge, but only few students with low representational competence and high conceptual knowledge.ConclusionsThese results support the assumption that representational competence is a prerequisite, yet insufficient condition for the acquisition of conceptual knowledge. We provide suggestions for supporting learners in building representational competence, and particularly female learners in utilizing their representational competence to build conceptual knowledge.

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

“…In addition, the authors found that in a scatter plot, there were almost no students with high conceptual knowledge but low representational competence, whereas there appeared to be more students with high representational competence but low conceptual knowledge (see Figure 1). From these results, the authors inferred the hypothesis that representational competence is a necessary yet insufficient prerequisite for developing conceptual knowledge (Edelsbrunner et al, 2022).…”

“…In the present study, we are concerned with the empirical relationship between representational competence and conceptual knowledge and, in particular, with the conditions that might influence the magnitude of this relationship. Although a strong positive relationship has been assumed by many researchers, it has been noted that the quantitative empirical evidence on this relationship is rather sparse and struggles with methodological issues (Chang, 2018;Edelsbrunner et al, 2022). As Edelsbrunner et al (2022) note, instruments that have been used to measure these two constructs and their interrelations often have not been psychometrically validated, or suffer from contextual bias.…”

“…Although a strong positive relationship has been assumed by many researchers, it has been noted that the quantitative empirical evidence on this relationship is rather sparse and struggles with methodological issues (Chang, 2018;Edelsbrunner et al, 2022). As Edelsbrunner et al (2022) note, instruments that have been used to measure these two constructs and their interrelations often have not been psychometrically validated, or suffer from contextual bias. Specifically, if instruments such as pen-and-paper tests that are assumed to measure the two constructs are both contextualized within the same topic (e.g., electromagnetism; Nieminen et al, 2013;Nitz et al, 2014;Scheid et al, 2019), then the common relation between the two constructs cannot be disentangled from such common topical context.…”

“…A recent study by Edelsbrunner et al (2022) tried to overcome these issues by using an assessment instrument for representational competence with field representations (such as vector-field plots and field lines) that does not employ an explicit topical context. Using this instrument, the authors investigated the relation between representational competence with fields and conceptual knowledge about electromagnetism in university undergraduates from Germany and Switzerland.…”

“…Specifically, whereas a scatter plot is an important and powerful visual tool, it does not prevent getting false impressions about patterns that might be driven by sampling error. Another reason for a model-based re-analysis is that the sample by Edelsbrunner et al (2022) encompassed four different student samples from different courses (teacher education, STEM and non-STEM study programs) at three different universities. The authors analyzed the students from all four samples within the same models, potentially hiding important information regarding the generalizability of their findings.…”