Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…

It is crucial nowadays to predict in a fast and simple manner physical-chemical behaviors like, the size-dependent optical properties of gold nanospheres (Au NSs). The idea behind this experiment is trying to replace (as much as possible) robust and expensive microscopy techniques with UV-vis spectrophotometry and friendly simulations. Students…

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least-squares (CLS), principal component…

Defines one approach to problem solving in terms of student use of algorithms to find their solutions and gives examples. Discusses how problems and algorithms relate to each other. Describes strategies for teaching problem solving using algorithms. (CW)

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)

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)

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)

Describes teaching strategies that help students improve problem solving skills. Lists three factors good problem solvers were found to possess. Gives step by step instructions for solving problems. (MVL)

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)

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)

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)