In this thesis, I discuss how the undergraduate computer scientist is trained, and how they learn what I am calling computational intuition. Computational intuition describes the methodology in which computer scientists approach their problems and solve them through the use of computers. Computational intuition is a series of skills and a way of thinking or approaching problems that students learn throughout their education. The main way that computational intuition is taught to students is through the experience they gain as they work on homework and classwork problems. To develop computational intuition, students learn explicit knowledge and techniques as well as knowledge that is tacit and harder to teach within the lectures of a classroom environment. Computational intuition includes concepts that professors and students discuss which include “computer science intuition,” “computational thinking,” general problem solving skills or heuristics, and trained judgement. This way of learning is often social, and I draw on the pedagogy of cognitive apprenticeship to understand the interactions between the professors, tutors, and other students help learners gain an understanding of the “computer science intuition.” It is this method of thinking that computer scientists at the Claremont Colleges have stated as being one of the most essential items that should be taught and gained throughout their education and signals a wider understanding of computer science as a field.
| Identifer | oai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:scripps_theses-1858 |
| Date | 01 January 2016 |
| Creators | Burke, Lauren |
| Publisher | Scholarship @ Claremont |
| Source Sets | Claremont Colleges |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Scripps Senior Theses |
| Rights | © 2016 Lauren N. Burke, default |
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