Shows that the rules of thumb for propagating significant figures through arithmetic calculations frequently yield misleading results. Also describes two procedures for performing this propagation more reliably than the rules of thumb. However, both require considerably more calculational effort than do the rules. (JN)

Describes an algorithm that provides more rapid convergence for more complicated forms of phase separation requiring the use of a digital computer. Demonstrates that this "inside-out" algorithm remains efficient for determination of the equilibrium states for any type of phase transition for a binary system. (CW)

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)

Reports on a study in which two spatial tests were given to science and engineering majors and to students in nursing and agriculture at Purdue University (Indiana). Scores from the tests consistently contributed a small but significant amount of success on measures of performance in chemistry. (TW)

Suggests a method for solving verbal problems in chemistry using a linguistic algorithm that is partly adapted from two artificial intelligence languages. Provides examples of problems solved using the mental concepts of translation, rotation, mirror image symmetry, superpositioning, disjoininng, and conjoining. (TW)

Reports on a study which examined the relationship between spatial ability and performance in organic chemistry. Results indicated that students with high spatial scores did significantly better on questions requiring problem solving skills, as well as on those requiring the mental manipulation of two-dimensional representations of a molecule. (TW)

Presents a new method to teach the subject of evaporators which is both simple enough to use in the classroom and accurate and flexible enough to be used as a design tool in practice. Gives an example using a triple evaporator series. Analyzes the effect of this method. (CW)

Differentiates between problems, exercises and algorithms. Discusses the role of algorithms in solving problems and exercises in chemistry. Suggests that very real differences exist between solving problems and exercises, and that problem solving steps can be and should be taught in chemistry education. (TW)

Examines the hypothesis that there are preliminary stages in problem solving that are often neglected in teaching chemistry. Discusses correlations calculated between the student's ability to handle disembedding and cognitive restructuring tasks in the spatial domain and ability to solve chemistry problems. (TW)

Described are ideas for the development of problem solving in the context of chemistry. Strategies for improving students' problem solving abilities are included. (KR)

Discusses some of the difficulties involved with chemistry laboratory experiences and some laboratory manuals. Cites studies that indicate that part of the difficulty can be attributed to constraints relating to the short-term memory of the operational information and the assumption that students have a certain level of knowledge. (TW)

Compares performances of students on gas-law problems that require two distinct approaches, either the algorithmic technique or the conceptual gestalt. Indicates that student effectiveness is considerably different utilizing each approach and that training or experience with the algorithm process should not be expected to facilitate the…

Defines M-demand as the maximum number of schemes that the subject must activate simultaneously in the course of executing a task. Discusses the effect of M-demand on problem solving. Uses algorithms to reduce M-demand. Describes the role of algorithms in problem solving. (MVL)