土工试验教学中如何培养学生的不确定性思维  

Training undergraduates to think with uncertainty in mind through soil mechanics laboratory testing

作　　者：吴兴征[1] 方有亮[1] 余莉[1] 冯震[1] 杜二霞[1] WU Xingzheng;FANG Youliang;YU Li;FENG Zhen;DU Erxia(College of Civil Engineering and Architecture, Hebei University,Baoding 071002, P. R. China)

机构地区：[1]河北大学建筑工程学院

出　　处：《高等建筑教育》2019年第3期122-130,共9页Journal of Architectural Education in Institutions of Higher Learning

基　　金：河北省高校新工科研究与实践项目(2017GJXGK003);河北省高等教育教学改革研究与实践项目(2017GJJG011)

摘　　要：土力学试验中测得的土性指标具有较大离散性,导致学生对试验结果缺乏信心。因此,有必要探讨如何在土工试验教学中培养学生的不确定性思维。为提高教学效果,结合统计语言R的绘图和科学计算功能,阐述土性指标统计特性。以颗粒分析、含水量、液塑限、渗透系数、压缩系数和剪切强度指标的多组平行试验结果为例说明不确定性的表述方法。采用概率密度分布曲线、非参数核密度估计、直方图、箱线图等对测试结果进行不确定性展示。该教学实践可以提高学生处理差异数据的综合判断与归纳能力。Along with the inherent scattering in soil physical and mechanical parameters of laboratory testing, it is difficult for students to sustain their conviction in their own tests. It is necessary to explore how to train students’ uncertainty thinking through soil mechanics laboratory testing. To improving teaching effect, statistical characteristics of soil parameters are expounded by the drawing and scientific computing functions of statistical programming language R. Multiple parallel experiments on various parameters, including particle size analysis, moisture content, liquid and plastic limits, permeability coefficient, compression coefficient and shear strength, are used to demonstrate how uncertainties can be quantified. Test results are presented using probability density distribution curves, non-parametric kernel density estimates, histograms, and box plots. Such teaching practice can enhance undergraduates’ comprehensive judgment and inductive ability in handling inconsistent data.

关 键 词：土性参数 不确定性 土工试验 R语言 

分 类 号：G642.423[文化科学—高等教育学] TU473[文化科学—教育学]