Success in using a computer in education as a problem-solving tool requires a change in the way of thinking or of approaching a problem. An algorithm, i.e., a finite step-by-step solution to a problem, can be designed around the data processing concepts of input, processing, and output to provide a basis for classifying problems. If educators…

A traditional question is whether or not computers shall ever think like humans. This question is redirected to a discussion of whether computers shall ever be truly creative. Creativity is defined and a program is described that is designed to complete creatively a series problem in mathematics. (MP)

Teaching which does not in any significant way address the genesis of mathematical ideas hides the fact that mathematics is created by people; that it involves intuition, exploring, conjecturing, and reasoning; and that it is purposeful. Such teaching can give students appearance that much of mathematics is very arbitrary; it just falls from the…

Describes the current status and limitations of expert systems, and explores the possible applications of such systems in education and training. The use of expert systems as tutors, as job aids, and as a vehicle for students to develop their own expert systems on specific topics are discussed. (40 references) (CLB)

Computer science education for the liberal arts student has both a practical value (creating an intelligent consumer) and an appreciative value (teaching algorithmic thinking). A computer literacy course can be structured to harmonize with the aims of liberal education. (SK)

Arguments for and against the use of computers in mathematics classes have centered on whether students benefit from or are merely hindered by practicing computational skills. This paper claims that the true essence of mathematics lies not in computation, basically a mechanical operation, but in problem-solving. Since no amount of computational…

Addresses the distinction between conceptual and algorithmic learning and the clarification of what is meant by a second-tier student. Explores why novice learners in chemistry and physics are able to apply algorithms without significant conceptual understanding. (DDR)

An attempt is made to show that algebra is rarely obvious, and merely expecting children to learn rules is an oversimplification. Sections cover: (1) The Non-visual Nature of Algebra; (2) The Apparently Arbitrary Nature of Algebra; (3) The Relationship Between Symbolism, System and Question; (4) The Complex Nature of Algebra; and (5) Some…

Research on the cognitive processes used by physicians during patient care (template matching, deductive logic starting with multiple hypotheses, and algorithmic logic) is examined for its applicability to optometrists and the problem-solving strategies used by optometric students in the classroom or clinic. (Author/MSE)

The need for incorporating algorithmics into mathematics instruction is presented. The proliferation of computers is seen to have made the designing of algorithms an essential skill. Examples are given, and the view that mathematics will lose much prestige and importance if algorithmics is not integrated into it is presented. (MP)

Changes in the elementary and junior high school mathematics curriculum that have occurred in the last 20 years and that may occur in the future are discussed. (MK)

Describes an algorithm that can be used for the construction of an oral presentation. The article suggests how nonnative English-speaking students can be taught to make their presentations more cohesive and to evaluate their cohesiveness with the help of video recordings. (21 references) (Author/CK)

Discusses the differences between problems and exercises in chemistry, and some of the difficulties that arise when the same methods are used to solve both. Proposes that algorithms are excellent models for solving exercises. Argues that algorithms not be used for solving problems. (TW)

Discusses the difference between a generic chemistry problem (one which can be solved using an algorithm) and a harder chemistry problem (one for which there is no algorithm). Encourages teachers to help students recognize these categories of problems so they will be better able to find solutions. (TW)

Discusses the differences between problems and exercises, the levels of thinking required to solve them, and the roles that algorithms can play in helping chemistry students perform these tasks. Proposes that students be taught the logic of algorithms, their characteristics, and how to invent their own algorithms. (TW)