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Assisted mathematics: the case of discrete probabilities

  • Anne Bergeron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)

Abstract

In this paper, we describe computer environments designed or used to assist learning in discrete probability theory: some with no ‘intelligence’, some with a lot. The degree of assistance ranges from a sophisticated dumb tool to a general problem solver. The main difference between the environments lies in the division of quality and quantity of work between the user and the computer. This leads to a discussion of what one is expected to learn in a certain field and what kind of tools should be provided to students. In particular, we are interested in what happens to the human/computer team when the computer ‘solves’ all the problems.

The field of discrete probabilities has a number of features that suggest this kind of discussion: a strong experimental component that can be easily linked to everyday experience, a simple and powerful theoretical background, and difficult problems for the novice. But the discussion is also intended to raise similar questions in other fields of mathematics and science: the mathematical problems that can be effectively solved by automatic means already include most of the problems non-mathematicians are expected to solve.

Keywords

Basic Skill Symbolic Computation Discrete Probability Exact Answer General Problem Solver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Anne Bergeron
    • 1
  1. 1.Département de mathématiques et d'informatiqueUQAMMontréalCanada

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