Explaining Algorithms using Metaphors [Forišek & Steinová 2013-04-10].pdf

SPRINGER BRIEFS IN COMPUTER SCIENCE

Michal Forišek · Monika Steinová

Explaining

Algorithms Using

Metaphors

SpringerBriefs in Computer Science

Series Editors

Stan Zdonik

Peng Ning

Shashi Shekhar

Jonathan Katz

Xindong Wu

Lakhmi C. Jain

David Padua

Xuemin Shen

Borko Furht

V. S. Subrahmanian

Martial Hebert

Katsushi Ikeuchi

Bruno Siciliano

For further volumes:

http://www.springer.com/series/10028

Michal Forišek

Monika Steinová

•

Explaining Algorithms

Using Metaphors

123

Michal Forišek

Department of Computer Science

Comenius University

Bratislava

Slovakia

Monika Steinová

Department of Computer Science

ETH Zürich

Zurich

Switzerland

ISSN

2191-5768

ISSN 2191-5776

(electronic)

ISBN 978-1-4471-5018-3

ISBN 978-1-4471-5019-0

(eBook)

DOI 10.1007/978-1-4471-5019-0

Springer London Heidelberg New York Dordrecht

Library of Congress Control Number: 2013932561

The Author(s) 2013

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Preface

Motivation

Feynman was a truly great teacher. He prided himself on being able to devise ways to

explain even the most profound ideas to beginning students. Once, I said to him, ‘‘Dick,

explain to me, so that I can understand it, why spin one-half particles obey Fermi-Dirac

statistics.’’ Sizing up his audience perfectly, Feynman said, ‘‘I’ll prepare a freshman

lecture on it.’’ But he came back a few days later to say, ‘‘I couldn’t do it. I couldn’t reduce

it to the freshman level. That means we don’t really understand it.’’

—David L. Goodstein

The above quotation nicely expresses our feelings about education. It is our

opinion that the ability to explain a subject is an indicator of having the deepest

understanding of the subject. Without knowing the subject like the back of one’s

hand, it is impossible to explain it in (relatively) simple terms. This is true for all

subjects, but perhaps most signiﬁcant when the subject is scientiﬁc.

In science, the research does sometimes bring a new discovery. The discovery

then initiates a wave of new results—both pushing the boundary further and ﬁlling

in the blanks around the initial discovery. But even once all the blanks have been

ﬁlled in, it still makes sense to continue doing the research. The goal of this phase

is to simplify the results as much as possible: to look for a more systematic

approach, to ﬁnd the most concise way of describing and presenting them, to ﬁnd

the simplest possible proof… In other words, to extract the essence. This phase of

research has two major beneﬁts: First, there is the pure scientiﬁc beneﬁt of

understanding the subject better, which often allows the researchers to discover

patterns that were invisible in the ﬁrst results, or to generalize or otherwise

improve the results. The second beneﬁt is for the sake of education. The better we

understand what’s going on, the easier we can explain it to the next generation of

researchers, the faster the research can move forward.

The history of science abounds with examples where a new, better way of

looking at things signiﬁcantly facilitated our progress. For instance, Euclidean

geometry was almost without any progress for centuries, until René Descartes

came up with the coordinate system. This discovery gave birth to analytic

geometry—and suddenly, we saw connections between geometry, algebra, and

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