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Using Argument-Driven Inquiry Learning to Improve Students' Mental Models

机译:使用论证驱动的查询学习改善学生的心理模型

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Many efforts on the improvement of students' mental models have been reported. However, many of them were only explore mental models which are possesed by students. In this work, we report the enhancing of students' mental models on heat and temperature deeper than those reported previously. We used the experiment class that study by argument-driven inquiry learning and the control class that study by conventional learning. The data were obtained through pre- and post-tests using mental models instrument. The resulted data were grouped into three categories, i.e., scientific, synthetic, and initial. We employed Mann-Whitney U test and cross-tabulation to evaluate the enhancement of students' mental models. We found a significant increase in post-test scores in the experiment class and the students' frequency in the scientific group. Furthermore, it was found that a total of students' post-test scores in the experiment class increased more significant than the control class. The difference in enhancement shows that argument-driven inquiry learning gives an impact better than conventional class. Furthermore, we also discuss differences in student's understanding.
机译:已经报道了许多关于提高学生心理模型的努力。然而,其中许多只是探索学生拥有的心理模型。在这项工作中,我们向学生的精神模型提升了比以前报告的那些更深的热量和温度。我们使用了通过参数驱动的查询学习和通过传统学习研究的控制类研究的实验课程。通过使用精神模型仪器通过预先测试和后测试获得数据。将产生的数据分为三类,即科学,合成和初始。我们雇用了Mann-Whitney U测试和交叉制表,以评估学生的心理模型的增强。我们在科学集团中发现了实验课程和学生频率的测试后得分显着增加。此外,发现实验课程中的总体测试后得分比对照类更大。增强的差异表明,参数驱动的查询学习产生的影响比传统类更好。此外,我们还讨论了学生的理解差异。

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