Computing Across Curricula: The View Of Industry Leaders

Wiebe, Eric N.; Ho, Chia-Lin; Raubenheimer, Dianne; Bullard, Lisa; Joines, Jeffrey A.; Miller, Carolyn; Rouskas, George N.

doi:10.18260/1-2--4918

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…The goal is to clarify constraints, parameters, and processes; critical thought processes vital to becoming a successful researcher or designer (Bundy, 2002;Craig et. al., 2008;Lunt and Ekstrom 2006;O'Neill, 1996;Vergara, 2009;Wiebe, 2009). Some previous work has been published on the idea of Coding to Think (Wing, 2008;Wing, 2006), but no publications were found on practical implementations of this theoretical idea.…”

Section: Project Motivationmentioning

confidence: 99%

“…Yet, the philosophical underpinnings and pedagogical goals of a computing course are often nebulous (Cordes 1997;Craig et al, 2008;Dunne et. al., 2005;Hambrusch 2009;O'Neill, 1996;Vergara, 2009;Wiebe, 2009). Should the course lean toward an advanced engineering mathematics course (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the course could be structured to teach algorithmic thought processes (Dunne et. al., 2005;Hambrusch, 2009;Musante, 2006;Von Lockette, 2006;Wiebe, 2009;Wing, 2008;Wing, 2006). No one way is best and any computing course should address all three to some extent.…”

Section: Introductionmentioning

confidence: 99%

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Coding to Think: Teaching Algorithmic Thinking from Idea to Code

Tranquillo¹

2015

Journal of Engineering Education Transformations

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Engineering computing is a topic that nearly all engineering departments include in their curricula. Yet, the pedagogical goals of a computing course are necessarily split between code as a means of learning higher level math, code as a specific tool in design and research, and code as a way to learn algorithmic thinking. Learning more advanced applied math is typically learned through the traditional lecture/homework/test format, whereas learning the syntax of a particular language is most often taught through short programming assignments. This paper introduces Coding to Think as a way to teach algorithmic thinking that builds off of the Writing to Think movement in the Humanities. This technique is very well suited to long-term proj ects as it provides an opportunity to focus on deeper and more complex algorithmic thinking. The semester-long project presented is motivated by three guiding learning outcomes: 1) To program at a level of complexity that requires planning, iteration, encapsulation and documentation, 2) To move from Idea to Code (a phrase that is mentioned in class at least once a week) and 3) To articulate and put into practice the power of a computing language that can do more than a calculator or Excel. The seven project assignments that lead students from an initial idea to final code are detailed, as well as an assessment of outcomes, student and faculty comments, suggested improvements and adaptations and ABET assessment measures.

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Section: Project Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coding to Think: Teaching Algorithmic Thinking from Idea to Code

Tranquillo¹

2015

Journal of Engineering Education Transformations

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“…The implementation of computing into TE 303 was part of the fellows program of the NSF grant, Computing Across Curricula. 4,5 The fellows program was an effort to create a community of faculty engaged in using computing in their courses through workshops, seminars, and action research projects. Figure 1 illustrates the computational capabilities model developed by Weibe and coworkers 5 as part of the NSF grant that provides the framework.…”

Section: Introductionmentioning

confidence: 99%

“…4,5 The fellows program was an effort to create a community of faculty engaged in using computing in their courses through workshops, seminars, and action research projects. Figure 1 illustrates the computational capabilities model developed by Weibe and coworkers 5 as part of the NSF grant that provides the framework. They recognized that certain innate intellectual capabilities are essential for problem solving, which include the general cognitive abilities necessary for learning and applying declarative and procedural knowledge.…”

Section: Introductionmentioning

confidence: 99%

Integrating Computing into Thermodynamics: Lessons Learned

Pasquinelli¹,

Joines²

2011 ASEE Annual Conference &Amp; Exposition Proceedings

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Her research expertise is in the design and application of computational approaches that predict and modulate the properties of systems at the nanoscale, including polymers, proteins, and fibers. (More information about her team and their research projects can be found at http://www.te.ncsu.edu/mpasquinelli.) She also teaches a variety of courses each year at the undergraduate and graduate levels on topics such as computer modeling, engineering thermodynamics, sustainability, and textile materials and systems. She was awarded a University Teaching Award in 2009 by NC State, and a Graduate Teaching Award in 1999 by Carnegie Mellon. In addition to her research and teaching activities, Dr. Pasquinelli has a long history of community outreach activities, which has included judging several regional and state science competitions a year, mentoring females and minorities interested in technical fields, serving as a mentor to K-12 science teachers, and presenting science-based workshops to students in middle school and high school.

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Is integration of communication and technical instruction across the SE curriculum a viable strategy for improving the real-world communication abilities of software engineering graduates?

Gannod

Anderson

Burge

et al. 2011

2011 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T)

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Software engineering educators and trainers are acutely aware that software engineering graduates need strong real-world communication abilities. The National Science Foundation is supporting a three-year project in which industry professionals, CS/SE faculty, and communication-across-the-curriculum specialists are collaborating to develop curricula and teaching resources designed to improve communication abilities of CS/SE graduates by integrating communication instruction and assignments with the technical work in courses across the students' four years of study. Our panelists-an industry practitioner, a CS/SE educator, and a communication specialist-will describe what has been learned in the project's first half and invite comments, insights and advice from the audience.

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Computing Across Curricula: The View Of Industry Leaders

Cited by 6 publications

References 9 publications

Coding to Think: Teaching Algorithmic Thinking from Idea to Code

Coding to Think: Teaching Algorithmic Thinking from Idea to Code

Integrating Computing into Thermodynamics: Lessons Learned

Is integration of communication and technical instruction across the SE curriculum a viable strategy for improving the real-world communication abilities of software engineering graduates?

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