| The purpose of this investigation (a quasi-experimental time-series design) was to identify and describe the differences in the methods used by experts (university chemistry professors) and nonscience major introductory chemistry students, enrolled in a course at the university level, to solve paired algorithmic and conceptual problems. Of the 180 students involved, the problem-solving schema of 20 novices were evaluated using a graphical method to dissect their think-aloud interviews into episodes indicative of solutions to paired problems on density, stoichiometry, bonding, and gas laws. These interviewed novices were classified into three different problem-solving categories (high algorithmic/high conceptual, high algorithmic/low conceptual, and low algorithmic/low conceptual), and composite graphs of their problem-solving schema were compared to those of the experts' category. Results of these comparisons indicated that there is an indirect relationship between a subjects' ability to solve problems, and the time and number of transitions required. As the subjects' ability to solve both algorithmic and conceptual problems improved, less time and fewer transitions between episodes of the problem-solving schema were required to complete the problems. Algorithmic-mode problems were more frequently solved correctly by all groups investigated, and algorithmic-mode problems always required more time and a greater number of transitions for completion than did conceptual-mode problems. Other results of this study replicated findings in the literature; namely, that there was a higher correlation between formal reasoning ability and conceptual problem-solving success than between formal reasoning ability and algorithmic problem-solving success, and that students' problem-solving category predicted algorithmic and overall success better than students' success on solving conceptually-based chemistry problems. Scores from the GALT test and SAT had only a minimal positive correlation with academic achievement (i.e., r =.24 and r =.30, respectively). Also, differences in achievement were seen between the students from the various colleges studied (i.e., Business, Communication, Engineering, Liberal Arts, and Natural Sciences). Students enrolled in nonscience major degree programs were more successful in this class for nonscience majors, than were their peers seeking a science or engineering degree; however, no differences were seen in any group of students' formal operational level, nor were any gender differences apparent. |
