An Investigation of Undergraduates’ Computational Thinking in a Sophomore-Level Biomedical Engineering Course

Shoaib, Huma; Cardella, Monica E.; Madamanchi, Aasakiran; Umulis, David M.

doi:10.1109/fie43999.2019.9028503

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…However, education research suggests that a narrow focus on technical competency typically provides students with routine expertise, but lacks the adaptive expertise needed to solve computational problems in real world settings [24][25][26]. For example, students attempting to develop computational models of cellular and molecular biological systems report challenges with higher-order computational thinking skills such as abstraction and problem decomposition, rather than coding or mathematics [10,27,28].…”

Section: Computational Apprenticeshipmentioning

confidence: 99%

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

Madamanchi

Thomas

Magana

et al. 2019

Preprint

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There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although individual institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remains a bottleneck. The opportunity is ripe for using education research principles to develop new mechanisms of cross-disciplinary training at the intersection of mathematics, computation and biology. In this paper we present a case study which describes the efforts of one computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences in alignment with the computational apprenticeship theoretical framework. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction, and cross-disciplinary scientific communication.

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Section: Computational Apprenticeshipmentioning

confidence: 99%

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

Madamanchi

Thomas

Magana

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is important to know "which components of computational thinking are difficult to learn" as a starting point to answer, "what to teach effectively" [9]. Abstraction and decomposition are identified to be the most difficult to learn conceptual misunderstandings of computational thinking [13].…”

Section: C) Learning Misconceptions Of Computational Thinking Componentsmentioning

confidence: 99%

A Literature-based Perspective Towards Learning and Pedagogy of Computational Thinking

Shoaib¹,

Brophy²

2020 ASEE Virtual Annual Conference Content Access Proceedings

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This literature review is a complete study which presents a new perspective to view computational thinking as an enduring competency for learning and problem-solving. Computational thinking is a 21st century way of problem-solving and is also a competency that has enduring outcomes given we are in the technological era. However, there is limited research synthesis available on how Computational Thinking enhances learning and what kind of pedagogies are suitable for incorporating Computational Thinking in science and engineering classrooms. We perform a literature review of the available research related to Computation Thinking to answer the following research questions: 1) What does the literature inform us about learning or transfer through Computational Thinking in science and engineering disciplines? 2) How can Computational Thinking based learning be facilitated through pedagogy? We also examine which components of Computational thinking are difficult to learn and why so? in-order to address our first research question. Hence, the key components of this review focus on learning and pedagogy based on Computational Thinking. We develop a synthesis of suggestions and explanations to answer the proposed questions based on literature from recent research in computational thinking. As for the instructional implication, based on our initial analysis we propose that a constructionism-based problem-solving active learning environment, with information processing, scaffolding and reflection activities, could be designed to enhance learning through computational thinking.

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Examining the role of computing identity in the computing experiences of women and racially minoritized undergraduates: a literature review

Clarke,

Mondisa,

Packard

et al. 2023

J Comput High Educ

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An Investigation of Undergraduates’ Computational Thinking in a Sophomore-Level Biomedical Engineering Course

Cited by 5 publications

References 8 publications

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

A Literature-based Perspective Towards Learning and Pedagogy of Computational Thinking

Examining the role of computing identity in the computing experiences of women and racially minoritized undergraduates: a literature review

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