Measuring Relational Reasoning

Alexander, Patricia A.; Dumas, Denis; Grossnickle, Emily M.; List, Alexandra; Firetto, Carla M.

doi:10.1080/00220973.2014.963216

Cited by 60 publications

(83 citation statements)

References 86 publications

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“…Unfortunately, a number of studies with students have shown that the discernment of meaningful patterns within information, however crucial and pervasive they may be, does not necessarily come easily to all learners (e.g., Alexander et al 2015;Krawczyk et al 2011). However, with continued efforts to understand the process of relational reasoning (e.g., Grossnickle et al 2016), as well as to support relational reasoning and associated abilities in classroom-based interventions (e.g., Murphy et al 2016), there is sufficient evidence to support the conclusion that relational reasoning is a malleable cognitive ability, capable of being taught to students.…”

Section: Relational Reasoning Is Important For Scientists Medical Prmentioning

confidence: 97%

“…Moreover, in psychometric research using cognitive measures (e.g., TORR; Alexander et al 2015;) that include items designed to tap each of the forms of relational reasoning, significant positive correlations at the latent variable level among the forms of relational reasoning are found. However, these correlations are not so large as to suggest that the forms of relational reasoning do not differ in measurable ways.…”

Section: The Forms Of Relational Reasoning Operate In Concert With Onmentioning

confidence: 98%

“…Therefore, some researchers in the field (e.g., Alexander et al 2015) describe relational reasoning as taking multiple forms depending on the type of pattern being recognized or reasoned with. For example, instances of structural similarity between and among concepts have been termed analogies (Richland and Simms 2015;Holyoak 2012).…”

Section: Relational Reasoning and Its Formsmentioning

confidence: 99%

“…Yet, within the field of educational psychology, part-time graduate students in STEM domains who are also working full-time in their field can be recruited in order to give some insight into the relational reasoning of working professionals in the in vitro setting. For example, Dumas and Schmidt (2015) used a psychometric measure (Test of Relational Reasoning; TORR; Alexander et al 2015) to predict the originality of just such graduate-level design engineers' solutions to design challenges. These researchers found a strong predictive relation between engineers' relational reasoning ability and the originality of their ideas.…”

Section: In Vitromentioning

confidence: 99%

“…For this reason, educational and psychological researchers interested in relational reasoning typically employ diverse methodologies for measuring relational reasoning ability, including psychometric measures and tasks (e.g., Alexander et al 2015;, naturalistic observations (e.g., Chan and Schunn 2015;Dumas et al 2014), and cognitive interview techniques (Jablansky et al 2015). There may be much still to learn about relational reasoning, and for this line of inquiry, the entire gamut of measurement tools must be used: from selected-response assessments and neuroimaging methods to semi-structured interviews, naturalistic observations, and computer simulations.…”

Section: Relational Reasoning Can Be Observed and Measured In Diversementioning

confidence: 99%

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Relational Reasoning in Science, Medicine, and Engineering

Dumas

2016

Educ Psychol Rev

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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.

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Section: Relational Reasoning Is Important For Scientists Medical Prmentioning

confidence: 97%

Section: The Forms Of Relational Reasoning Operate In Concert With Onmentioning

confidence: 98%

Section: Relational Reasoning and Its Formsmentioning

confidence: 99%

Section: In Vitromentioning

confidence: 99%

Section: Relational Reasoning Can Be Observed and Measured In Diversementioning

confidence: 99%

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Relational Reasoning in Science, Medicine, and Engineering

Dumas

2016

Educ Psychol Rev

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Inferring task performance and confidence from displays of eye movements

Emhardt

Wermeskerken

Scheiter

et al. 2020

Applied Cognitive Psychology

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Eye movements reveal what is at the center of people's attention, which is assumed to coincide with what they are thinking about. Eye-movement displays (visualizations of a person's fixations superimposed onto the stimulus, for example, as dots or circles) might provide useful information for diagnosing that person's performance. However, making inferences about a person's task performance based on eyemovement displays requires substantial interpretation. Using graph-comprehension tasks, we investigated to what extent observers (N = 46) could make accurate inferences about a performer's multiple-choice task performance (i.e., chosen answer), confidence, and competence from displays of that person's eye movements. Observers' accuracy when judging which answer the performer chose was above chance level and was higher for displays reflecting confident performance. Observers were also able to infer performers' confidence from the eye-movement displays; moreover, their own task performance and perceived similarity with the performer affected their judgments of the other's competence.

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Development and calibration of the student opportunities for deeper learning instrument

Dumas

Dong

2019

Psychology in the Schools

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This article describes the development and calibration of the student opportunities for deeper learning instrument (SODLI). The SODLI is designed to measure the amount of learning opportunities intended to support higher‐order thinking that students are exposed to through student self‐report, and is expected to be widely useful in psychological research in schools. Here, the psychometric functioning of the SODLI is investigated in a relatively large sample of U.S. high‐school students (N = 963). Using multidimensional item‐response theory methods, a nine‐factor correlated model is determined to fit the data best, and to produce reliable estimates of SODLI dimensions. The SODLI is also shown to exhibit scalar‐invariant measurement properties across sex, race/ethnicity, and language‐background groups, although latent mean differences on some of the SODLI dimensions across those groups were identified.

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Measuring Relational Reasoning

Cited by 60 publications

References 86 publications

Relational Reasoning in Science, Medicine, and Engineering

Relational Reasoning in Science, Medicine, and Engineering

Inferring task performance and confidence from displays of eye movements

Development and calibration of the student opportunities for deeper learning instrument

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