Relational Reasoning in Science, Medicine, and Engineering

Abstract

This review brings together the literature that pertains to the role of relational reasoning, or the ability to discern meaningful patterns within any stream of information, in the mental work of scientists, medical doctors, and engineers. Existing studies that measure four forms of relational reasoning-analogy, anomaly, antinomy, and antithesis-are included in this review. These studies are organized into four groups based on their general measurement paradigm: those that use naturalistic observation methods to study relational reasoning in vivo; those that take a historical perspective to describe the construct as it arises in recordo; those that manipulate relevant variables in an in vitro or laboratory setting; and those that formulate computer models and algorithms to simulate relational reasoning in silico. Principal findings from this literature are presented and discussed, as are enduring questions about the nature and consequences of relational reasoning.

Sketching and verbal self‐explanation: Do they help middle school children solve science problems?

“…Thinking about relationships between elements and then coming up with hypotheses about processes between elements is referred to as relational reasoning . Structure‐mapping theory (Gentner, 1983) argues that relational reasoning requires mapping between elements of a domain or determining relations between concepts (Dumas, 2017) or elements. For this to be successful, a series of steps must be met.…”

Exploring Potential Educational and Social Contributors to Relational Reasoning Development

“…For one, past decades of in vitro and in vivo research have consistently shown that these forms follow somewhat different developmental trajectories. For educational research, in vitro studies are more laboratory‐type investigations where conditions are carefully controlled or regulated, whereas in vivo studies involve the more naturalistic observation of phenomena (Dumas, 2017). For example, in an in vivo longitudinal study that involved analysis of verbalizations made during novel task performance (Jablansky, Alexander, Dumas, & Compton, 2016, 2019), younger children (ages 4–8) relied more on analogical reasoning and anomalous reasoning.…”

Development and calibration of the student opportunities for deeper learning instrument

“…In specifying the general construct of student opportunities for deeper learning, nine contexts or domains in which student opportunities for deeper learning have been shown to occur were identified: (a) complex problem solving, (b) creative thinking, (c) communication, (d) collaboration, (e) learning to learn, (f) teacher feedback, (g) assessments aligned with deeper learning, (h) interdisciplinary learning, and (i) real‐world connections. These nine contexts and domains in which student opportunities for deeper learning are conceptualized to potentially arise were drawn from the extant literature on higher‐order reasoning (e.g., Dumas, ; Richland & Simms, ), creative problem solving (Puccio et al, ), critical‐analytic thinking (Byrnes & Dunbar, ), and 21st‐century skills (Woods‐Groves & Choi, ), in which these nine areas are generally described as holding the potential for deeper learning to occur.…”