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Thomas Kraska – Journal of Chemical Education, 2022
An educational lattice model is proposed for the investigation of the influence of the density and indirectly of the pressure on the chemical equilibrium of the ideal gas phase reaction A [equilibrium] 2B. The model can be introduced by a board game simulating a stochastic process. This game can also be used to set up a corresponding computer…
Descriptors: Secondary School Students, Secondary School Science, Chemistry, Science Instruction

Thomson, Norman; Stewart, James – Journal of Biological Education, 1985
Explains an algorithm which details procedures for solving a broad class of genetics problems common to pre-college biology. Several flow charts (developed from the algorithm) are given with sample questions and suggestions for student use. Conclusions are based on the authors' research (which includes student interviews and textbook analyses).…
Descriptors: Algorithms, Biology, Genetics, Learning Strategies

Frank, David V.; And Others – Journal of Chemical Education, 1987
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)
Descriptors: Algorithms, Chemistry, College Science, Higher Education

Middlecamp, Catherine; Kean, Elizabeth – Journal of Chemical Education, 1987
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)
Descriptors: Algorithms, Chemistry, College Science, Higher Education

Schrader, C. L. – Journal of Chemical Education, 1987
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)
Descriptors: Algorithms, Chemistry, College Science, Higher Education

Stewart, Jim; Dale, Michael – Science Education, 1989
Investigates high school students' understanding of the physical relationship of chromosomes and genes as expressed in their conceptual models and in their ability to manipulate the models to explain solutions to dihybrid cross problems. Describes three typical models and three students' reasoning processes. Discusses four implications. (YP)
Descriptors: Algorithms, Biology, Concept Formation, Fundamental Concepts

Bodner, George M. – Journal of Chemical Education, 1987
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)
Descriptors: Algorithms, Chemistry, College Science, Higher Education

Pickering, Miles – Journal of Chemical Education, 1987
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)
Descriptors: Algorithms, Chemistry, College Science, Higher Education

Niaz, Mansoor; Robinson, William R. – Research in Science and Technological Education, 1992
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…
Descriptors: Algorithms, Chemistry, Cognitive Ability, Cognitive Style

Niaz, Mansoor – Journal of Chemical Education, 1989
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)
Descriptors: Algorithms, Chemistry, Cognitive Development, Cognitive Processes