Database vs. Expert System Teaching Paradigms: Using Organic Reaction Mechanisms To Teach Chemical Intuition

Paul H. Scudder
Division of Natural Sciences, New College of USF, Sarasota, FL 34243
J. Chem. Educ., 1997, 74 (7), p 777
DOI: 10.1021/ed074p777
Publication Date (Web): July 1, 1997

Abstract

A database and an expert system can serve as useful paradigms for the analysis of radically different teaching approaches. A content-driven lecture that relays facts and concepts to be memorized and regurgitated and treats each student as a database to be filled does not teach students how to think. To teach them to think like experienced chemists, one must focus on the problem solving process, similar to what is done in expert system design. This paper presents an expert-system-designed course that has worked well to teach students how to formulate reasonable hypotheses in organic chemistry. The course organizes reactants into generic electron sources and sinks, and treats reaction mechanisms as assemblies of a limited number of recognizable mechanistic steps. In class, the students are given a mechanistic puzzle; their search for an answer resembles a 'tree search' process. They learn and internalize a 'best-first search' – generate all possibilities, select the best one, then repeat for the next step. The 'control knowledge' used to select the best alternative includes: the DeltapKa rule, Coulomb's law, an electron flow check, a proton transfer Keq calculation, stability trends, reactivity trends, a pKa span check, a media pH check, a DeltaH of reaction calculation, the HSAB principle, checking for access and alignment, etc. Students are actively and frequently confronted in class and on exams with problems that require them to make decisions using this control knowledge. They develop a good chemical intuition and a reliable method for analyzing complex systems.

Keywords (Audience):

Second-Year Undergraduate

Keywords (Domain):

Organic Chemistry

Keywords (Pedagogy):

Problem Solving / Decision Making

Keywords (Subject):

Learning Theories

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  • Received: August 03, 2009

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