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Differences between algorithmic and conceptual problem-solving by nonscience majors in introductory chemistry.

机译:非化学专业的入门化学的算法和概念问题解决之间的差异。

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摘要

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.
机译:这项研究(准实验时间序列设计)的目的是确定并描述专家(大学化学教授)和非科学专业的基础化学学生(在大学课程中就读)所使用的方法之间的差异,并解决配对的算法和概念问题。在所涉及的180名学生中,使用图形方法评估了20名新手的问题解决方案,以将他们的思考方式访谈分解为情节,以表示对密度,化学计量,键合和气体定律等成对问题的解决方案。这些受访新手被分为三个不同的问题解决类别(高算法/高概念,高算法/低概念,低算法/低概念),并将他们的问题解决方案组合图与专家的解决方案作了比较。类别。这些比较的结果表明,受试者解决问题的能力与所需过渡的时间和数量之间存在间接的关系。随着受试者解决算法和概念问题的能力得到提高,解决问题方案的各阶段之间需要更少的时间和更少的过渡来完成问题。所有研究的群体都更正确地解决了算法模式问题,并且与概念模式问题相比,算法模式问题总是需要更多的时间和更多的转换来完成。这项研究的其他结果重复了文献中的发现。也就是说,形式推理能力和概念性问题解决成功之间的相关性高于形式推理能力和算法性问题解决成功之间的相关性,并且学生的问题解决类别预测的算法和整体成功要比学生在解决问题上的成功更好基于概念的化学问题。 GALT测验和SAT的分数与学业成绩之间的关系极小(分别为r = .24和r = .30)。此外,在所研究的各所大学(即商务,传播,工程,文科和自然科学)的学生之间也发现了成绩差异。与那些寻求科学或工程学位的同龄人相比,参加非科学专业学位课程的学生在该课程中对非科学专业的学习更为成功。但是,在任何一组学生的正式操作水平上都没有发现差异,也没有明显的性别差异。

著录项

  • 作者

    Mason, Diana Sue.;

  • 作者单位

    The University of Texas at Austin.;

  • 授予单位 The University of Texas at Austin.;
  • 学科 Education Educational Psychology.;Education Sciences.
  • 学位 Ph.D.
  • 年度 1994
  • 页码 191 p.
  • 总页数 191
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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