In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

Individual differences in reasoning: Implications for the rationality debate?-Open Peer Commentary-Differences, games, and pluralism

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The Cambridge handbook of expertise and expert performance

We introduce a conceptual framework of K-12 STEM literacy that rightfully and intentionally positions each and every student, particularly minoritized groups, as belonging in STEM. In order to conceptualize the equity-based framework of STEM literacy, we conducted a systematic review of literature related to STEM literacy, which includes empirical studies that contribute to STEM literacy. The literature on the siloed literacies within STEM (i.e., science, technology, engineering, and mathematics literacy) also contributed to formulate the necessity of and what it means to develop STEM literacy. The Equity-Oriented STEM Literacy Framework illuminates the complexities of disrupting the status quo and rightfully transforming integrated STEM education in ways that provide equitable opportunities and access to all learners. The Equity-Oriented STEM Literacy Framework is a research-based, equity and access-focused framework that will guide research, inform practice, and provide a lens for the field that will ensure each and every student, especially minoritized students, develop, and are developing STEM literacy.

/pdf/equity-oriented-conceptual-framework-for-k-12-stem-literacy-2r4v7qapv8.pdf

Equity-Oriented Conceptual Framework for K-12 STEM literacy

Computational thinking is an essential skill for full participation in society in today’s world (Wing, 2006). Yet there has been little discussion about the teaching and learning of computational thinking to English learners. In this paper, we first review what computational thinking is, why it is important in education, and the particular challenges faced in teaching computational thinking to speakers of English as a second language. We then discuss some approaches for addressing these challenges, giving examples from two recent K–12 initiatives in which we have been involved.

/pdf/teaching-computational-thinking-to-english-learners-3fzr03xgqm.pdf

Teaching computational thinking to English learners

Seeing themselves represented in the role models they aspire to, has been shown to be important to students' sense of belonging and success. Underrepresented college students in STEM fields are exposed to only a small set of role models. This set often consist of famous individuals with extraordinary stories (we call these outliers), and represent unfeasible paths to success for a large majority of these students. We aim to remedy this by identifying a set of role models who represent more feasible paths to success (we call these non-outliers) for many underrepresented students. We contend that, despite the less extraordinary success and stories of non-outliers, they share important qualities with outliers. We envision a “People Like Me” website based on profiles of this broader set of role models that can be used as a tool for recruitment and retention. Our current work is to (1) identify role model qualities from the perspective of students, (2) identify and create profiles of non-outlier role models based on these qualities, and (3) test if students are accepting of these non-outliers as potential role models. We have completed steps (1) and (2), and have found that non-outliers do exhibit the qualities our student sample pool seeks in role models.

/pdf/people-like-me-increasing-likelihood-of-success-for-2fvmr9ixok.pdf

People like me increasing likelihood of success for underrepresented minorities in STEM by providing realistic and relatable role models

This Research Category – Full Paper presents initial emergent themes from our quest to understand the construct of intuition. Our work uses theories of expertise development and dual-cognitive processing frameworks to provide a theoretical grounding to define discipline-specific intuition. We hypothesize that intuition can be observed in disciplinary experts through discussions of experience and decision-making processes. Interviews were conducted with professionals in three fields – engineering, nursing, and business management – that engage intuition in decision-making. A comparative analysis of emergent themes is presented to understand similarities and differences in use and definition across these disciplines. Parallel grounded theory and critical incident technique approaches were used to identify perceptions and incidents of intuition. Results suggest that intuition can be defined as a "sense of knowing" that is context specific and at least partly attributable to experience. Inclusion of multiple fields and comparisons across disciplines form the foundation for our future work focusing solely on engineering intuition.

Nurses, Managers, and Engineers – Oh My! Disciplinary Perceptions of Intuition and Its Role in Expertise Development

A defining characteristic of expertise is the use of intuition to navigate tasks. The construct of intuition and its importance is well‐studied in other disciplines, but little is known about how it translates to engineering. Existing literature on intuition does not clearly define the construct and its relationship to problem solving, which creates a substantial gap in our understanding of intuition and its applicability to engineering.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jee.20521

Situating intuition in engineering practice

Is the Answer Reasonable or Ridiculous? Common Factors among Students Who Display High Engineering Intuition on Technology-aided Solutions Elif Miskioglu, Bucknell University Elif Miskioglu is currently an Assistant Professor of Chemical Engineering at Bucknell University. She graduated from Ohio State University in 2015 with a PhD in Chemical Engineering, and is interested in student learning in engineering. Prof. Kaela Mae Martin, Embry-Riddle Aeronautical University, Prescott Kaela Martin is an Assistant Professor of Aerospace Engineering at Embry-Riddle Aeronautical University, Prescott Campus. She graduated from Purdue University with a PhD in Aeronautical and Astronautical Engineering and is interested in increasing classroom engagement and student learning.

Is the Answer Reasonable or Ridiculous? Common Factors among Students Who Display High Engineering Intuition on Technology-aided Solutions

Work In Progress: Experts’ Perceptions of Engineering Intuition

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