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Iteratively Intervening with the “Most Difficult” Topics of an Algorithms and Complexity Course

Iteratively Intervening with the “Most Difficult” Topics of an Algorithms and Complexity Course Iteratively Intervening with the "Most Difficult" Topics of an Algorithms and Complexity Course ¨ EMMA ENSTROM and VIGGO KANN, KTH Royal Institute of Technology When compared to earlier programming and data structure experiences that our students might have, the perspective changes on computers and programming when introducing theoretical computer science into the picture. Underlying computational models need to be addressed, and mathematical tools employed, to understand the quality criteria of theoretical computer science. Focus shifts from doing to proving. Over several years, we have tried to make this perspective transition smoother for the students of a third-year mandatory algorithms, data structures, and computational complexity course. The concepts receiving extra attention in this work are NP-completeness, one of the most central concepts in computer science, and dynamic programming, an algorithm construction method that is powerful but somewhat unintuitive for some students. The major difficulties that we attribute to NP-completeness are that the tasks look similar but have a different purpose than in algorithm construction exercises. Students do not immediately see the usefulness of the concept, and hence motivation could be one issue. One line of attacking NP-completeness has been to emphasize its algorithmic aspects using typical tools for teaching http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computing Education (TOCE) Association for Computing Machinery

Iteratively Intervening with the “Most Difficult” Topics of an Algorithms and Complexity Course

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1946-6226
DOI
10.1145/3018109
Publisher site
See Article on Publisher Site

Abstract

Iteratively Intervening with the "Most Difficult" Topics of an Algorithms and Complexity Course ¨ EMMA ENSTROM and VIGGO KANN, KTH Royal Institute of Technology When compared to earlier programming and data structure experiences that our students might have, the perspective changes on computers and programming when introducing theoretical computer science into the picture. Underlying computational models need to be addressed, and mathematical tools employed, to understand the quality criteria of theoretical computer science. Focus shifts from doing to proving. Over several years, we have tried to make this perspective transition smoother for the students of a third-year mandatory algorithms, data structures, and computational complexity course. The concepts receiving extra attention in this work are NP-completeness, one of the most central concepts in computer science, and dynamic programming, an algorithm construction method that is powerful but somewhat unintuitive for some students. The major difficulties that we attribute to NP-completeness are that the tasks look similar but have a different purpose than in algorithm construction exercises. Students do not immediately see the usefulness of the concept, and hence motivation could be one issue. One line of attacking NP-completeness has been to emphasize its algorithmic aspects using typical tools for teaching

Journal

ACM Transactions on Computing Education (TOCE)Association for Computing Machinery

Published: Jan 6, 2017

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