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首页> 外文会议>Canadian Society for Civil Engineering annual conference >GRADUATE ATTRIBUTE 3.1.3: INVESTIGATIONS - HOW CIVIL ENGINEERING GRADUATES CAN POSSESS THIS ATTRIBUTE
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GRADUATE ATTRIBUTE 3.1.3: INVESTIGATIONS - HOW CIVIL ENGINEERING GRADUATES CAN POSSESS THIS ATTRIBUTE

机译：研究生属性3.1.3：调查 - 土木工程毕业生如何拥有这种属性

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This presentation is based on a paper previously published and presented at the Canadian Engineering Education Association Conference in Winnipeg, 2012 (Lye, 2012). However it seems little has changed despite the warnings that engineering programs in Canada will be hard-pressed to meet the CEAB attribute 3.1.3 - Investigations. It is hope that with this presentation, more engineers will be aware of the problem and hopefully something can be done to remedy the situation. This executive summary is based on Lye (2012). Engineering programs in Canada are now evaluated by CEAB using an outcome-based approach (CEAB, 2010). Institutions must demonstrate that their graduates possess 12 specific graduate attributes at the time of graduation. These attributes are very similar to the list of 11 student outcomes as specified in (ABET, 2011). One of the graduate attributes (3.1.3) is "Investigations" which is defined as "an ability to conduct investigations of complex problems by methods that include appropriate experiments, analysis and interpretation of data, and synthesis of information in order to reach valid conclusions." This is similar to one of ABET's student outcomes which states that students attain "an ability to design and conduct experiments, as well as to analyze and interpret data". In this presentation, it will be argued that with the current curriculum of most if not all engineering schools in Canada, it is almost impossible for graduates to possess that stated attribute unless a compulsory course with appropriate labs and projects is introduced to specifically teach the proper methodologies for the design and analysis of experiments. Doing standard lab exercises where the instructor has given the objective, procedure and analysis methods, do not provide the foundations to learn proper design, conduct, and analysis of experiments of complex problems. All the student has to do is to follow instructions. Engineering educators and graduates thinking that somehow that the skill to design, conduct and analyze experiments will somehow be learned in an engineering program, probably do not appreciate fully the myriad of issues that are involved with experimentation to study a complex problem. Below is a sample list of issues that may be encountered in practice when dealing with typical engineering problems with some complexity. 1. Budget and time are always limited. Each experimental run may be expensive and/or time consuming. 2. Large number of factors or variables, some may be discrete, continuous or categorical. Some of the factors may or may not be controllable by the experimenter. 3. Some factors may interact with other factors although they may not be important on their own. 4. Several responses that may or may not be related. Responses may be discrete, continuous, time dependent, or even categorical. 5. Multiple objectives involved. Some responses may be more important than others, and some may be conflicting. One response may need to be maximized while another may need to be minimized or may need to be between certain limits. 6. Responses may be linear or nonlinear functions of the various factors. 7. Functional relationships between the responses and the factors may be desired for prediction and for design optimization purposes. a. Some factors may be hard to change and hence minimizing such changes would be more cost effective. b. Some combinations of factors may not be feasible or even dangerous. c. Measurement of responses may require high precision and/or a complex set up. 8. Firm grasp of theory may be important or there is no known theory to guide the experiment. 9. Responses may be dependent on the proportions of individual components that sum to a given value. 10. Extraneous factors may affect responses and lead to misleading results if not taken into account. 11. Experiments may involve people or animals. 12. There may be missing or bad data from the experiments. From the above list, it is clear that the numerous
机译：该演示是基于先前公布，并在加拿大工程教育协会会议在温尼伯，2012（碱液，2012）提交了一份文件。调查 - 不过似乎很少，尽管有这些警告，在加拿大工程项目将是捉襟见肘，以满足CEAB属性3.1.3改变。这是一个希望与此呈现，更多的工程师会意识到这个问题，并希望事情可以做，以纠正这种情况。该执行摘要是基于碱液（2012）。加拿大的工程项目现在由CEAB使用基于结果的方法（CEAB，2010）评估。机构必须证明他们的毕业生具备在毕业的时候12点具体研究生的属性。这些属性都非常相似，如（ABET，2011）规定的11项学生成果清单。一个研究生属性（3.1.3）是被定义为“通过包括适当的实验中，分析和解释数据，以及信息合成，以达到有效的结论的方法来进行的复杂的问题调查的能力“调查” “。这类似于ABET的学生成果之一其中规定，学生达到“设计和进行实验，以及分析和解释数据的能力。”在这个演示中，它将被认为与目前大多数的课程，如果不是在加拿大的所有工程学校，这几乎是不可能的，除非有合适的实验室和项目的必修课引入专门教导正确的毕业生拥有该声明属性方法为实验的设计和分析。做标准实验室练习里，老师给了目标，过程和分析方法，不提供基础，以学习正确的设计，实施和复杂问题的实验分析。所有学生必须做的就是按照指示。工程教育和毕业生认为莫名其妙的技能设计，实施和分析实验会以某种方式在工程计划可以得知，可能不完全明白那是参与实验来研究复杂的问题的问题无数。下面是可以在实践中典型的工程问题具有一定的复杂性交易时可能遇到的问题的样本名单。 1.预算和时间总是有限的。每个实验运行可能是昂贵和/或耗时的。 2.因素或变量大量，一些可以是离散的，连续或分类。有些因素可能是也可能不是由实验者控制。 3.有些因素可能与其他因素相互作用，尽管他们未必对自己很重要的。 4.可能会或可能不会进行相关的一些答复。响应可以是离散的，连续的，依赖于时间的，或甚至分类。 5.多重目标的参与。一些反应可能会比其他人更重要，有些可能是相互冲突的。一个响应可能需要被最大化，而另一个可能需要被最小化或可能需要在一定的范围之间。 6.响应可能是各种因素的线性或非线性函数。 7.响应和各因素之间函数关系可以被期望用于预测和用于设计优化的目的。一种。有些因素可能很难改变，因此最大限度地减少这样的变化会更划算。湾的因素的一些组合可能是不可行的或者甚至是危险的。 C。的响应的测量可能需要高精确度和/或一组复杂起来。 8.理论的牢固掌握可能是重要的或没有已知的理论来指导实验。 9.响应可依赖于个别组件之和给定值的比例。 10.因外部因素可能会影响响应，导致如果不考虑误导性的结果。 11.实验可能涉及到人或动物。 12.有可能丢失或实验坏数据。从上面的列表中，很显然，众多

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