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According to the summary data of 1992, only 1,092 of the 38,814 Ph.D.'s awarded in the USA went to African-Americans. The situation is even worse in the sciences. In the same year, only 17 African-Americans were awarded Ph.D. degrees in chemistry in the USA (1). Considering the growing minority population in the USA, it is imperative that a more effective delivery system be designed to attract more African-Americans into the chemical sciences. Recently, several chemistry educators (2 - 6) found that a good algorithmic problem solver may have limited understanding of the chemistry behind the algorithmic manipulations and that many bright students who have the ability to study chemistry are not attracted to the area (so-called "second-tier students"). However, most reported results were from a universities with primarily nonminority student populations. Therefore, a study of concept learning versus problem solving at a predominantly minority institution such as Florida A&M University (FAMU) may provide useful information to individuals considering using a more concept-based framework in their teaching.
In the present study, we adopted paired questions from Nakhleh's paper (6). These questions could help to identify the second-tier students in general chemistry classes by studying differential performance on conceptual and algorithmic questions. Each of the five pairs of questions deals with a particular area of general chemistry (gas laws, equations, limiting reagents, empirical formulas, and density). Within each pair, other than algorithmic and conceptual questions, a third questionnaire was added to ask students about their preference for either algorithmic or conceptual problems.
During years of teaching in both majority and minority institutions, the authors have observed that a large proportion of minority students are more interested in concepts than in algorithmic aspects of chemistry problems. The authors assumed that FAMU students might contain a relatively higher concentration of conceptual thinkers than of algorithmic problem solvers. Other quantitative characteristics were also preliminarily explored.
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