利用机器学习预测工科学生计算机辅助设计的成功
Using Machine Learning to Predict Engineering Technology Students' Success with Computer Aided Design
摘要：
计算机辅助设计（CAD）程序对工程至关重要，因为它们允许通过低成本迭代进行更好的设计。虽然CAD程序通常作为一项工作技能教授给本科生，但此类软件也可以帮助学生学习工程概念。CAD程序（即使是专门为教育目的设计的程序）的当前限制是它们不能为学生提供自动化的实时帮助。为了鼓励CAD程序为学生提供帮助，我们使用学生使用免费开源CAD软件Aladdin生成的数据来演示学生数据如何与机器学习技术相结合，以预测特定学生在设计任务中的表现。我们要求学生设计一个零能耗的房子，作为工程技术本科入门课程的一部分。使用来自128名学生的数据，以及scikit-learn Python机器学习库，我们使用设计动作的总计数和设计动作的序列作为输入来测试我们的模型。我们发现，我们的模型使用早期的设计序列动作是特别有价值的预测。我们的逻辑回归模型实现了>60%的机会预测学生是否会成功设计零净能源房屋。我们的研究结果表明，这将是可行的阿拉丁提供有用的反馈给学生时，他们大约一半通过他们的设计。对这些模型的进一步改进可能会导致更早的预测，从而更快地为学生提供反馈，以加强他们的学习。
英文摘要：
Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
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