How engineering students use domain knowledge when problem‐solving using different visual representations

Abstract

How engineering students use domain knowledge when problem solving using different visual representationsBackground: Engineering students commonly learn domain knowledge by engaging with visual representations of it. However, at times they have trouble accessing information from these representations due to the way information is encoded in features of the representation.Purpose: To describe how students engage with representation features, we explored two research questions: 1) What is the interplay between how concepts are encoded within representations, students' use of those concepts, and how students translate between representations during their problem solving and 2) How is the interplay described in Research Question 1 similar and different across students in statics versus those in digital logic? Design/Method: We synthesized findings from two of our prior research studies using the constant comparative method. We describe the effect of representations on students' ability to access and use domain knowledge during problem solving within and across engineering disciplines. Results:We identified three themes that describe how visual representations affect students' reasoning. First, students conflated concepts that were represented using similar features.

Realistic visualizations can aid transfer performance: Do distinctive shapes and descriptive labels contribute towards learning?

“…For all analyses in this paper, the assumptions of analyses of variance (ANOVAs), namely the normal distribution of residuals and variance homogeneity, were checked using Shapiro-Wilk tests and Levene's test, respectively. The learning data from Experiment 1 (see Figure 2) were analysed using a mixed 2 Â 2 ANOVA and both assumptions were met (with non- an important aspect that learners rely on in their cognitive processing (see Johnson-Glauch et al, 2020; see also Itti & Koch, 2000). Consequently, it was assumed that a higher level of shape distinctness helps both retention and transfer performance compared to renderings with a low shape distinctness.…”

“…As a result, they often exhibit a lower level of shape distinctness. The perceptual saliency of visualizations represents an important aspect that learners rely on in their cognitive processing (see Johnson‐Glauch et al, 2020; see also Itti & Koch, 2000). Consequently, it was assumed that a higher level of shape distinctness helps both retention and transfer performance compared to renderings with a low shape distinctness.…”

Spatial Display Model of Oil Painting Art Based on Digital Vision Design

“…Students may confuse concepts represented by similar features and not use concepts without salient features, and statics students are more coordinated in switching representations. These findings provide a generic domain pathway for redesigning the notation and representation of engineering concepts and suggest future research directions [21].…”

Theorizing Engineering Judgment at the Intersection of Decision-Making and Identity

“…To support the cognitive components, educational experiences should help emerging engineers develop the capacities to interpret ill-structured or ambiguous situations in ways that allow them to collaboratively or independently, to i) identify potential pattern matches; ii) distinguish typical versus anomalous situations; iii) apply appropriate mental models that allow them to simulate outcomes of choices; iv) monitor their own and their group's thinking and the evolving situation; and, v) manage uncertainty and ambiguity. To that end, current research identifying effective practices for helping students build mental models (e.g., Johnson-Glauch et al 2020;Streveler et al 2014;Yang et al 2020), develop expertise (e.g., Litzinger et al 2011;McKenna 2014), and engage in metacognition (P. J. Cunningham et al 2018;Magana et al 2019) can and should directly inform educators in designing relevant experiences.…”