Automatic Assessment of Students’ Engineering Design Performance Using a Bayesian Network Model
Automatic Assessment of Students’ Engineering Design Performance Using a Bayesian Network Model
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DOI：
10.1177/0735633120960422
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发表时间：
2020-09
影响因子：
4.8
通讯作者：
Wanli Xing;Chenglu Li;Guanhua Chen;Xudong Huang;J. Chao;Joyce Massicotte;Charles Xie
中科院分区：
文献类型：
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作者：
Wanli Xing;Chenglu Li;Guanhua Chen;Xudong Huang;J. Chao;Joyce Massicotte;Charles Xie
随着工程被纳入许多STEM教育标准，将工程设计纳入K-12课程变得越来越重要。然而，工程设计的结构不合理和开放的性质使得教师很难同时跟踪所有学生的设计过程并及时提供个性化的反馈。本研究提出了一个贝叶斯网络模型来动态、自动地评估学生对工程设计任务的投入程度，并支持形成性反馈。具体地说，我们将贝叶斯网络应用于111名九年级学生的过程数据，这些数据是由学生用来解决工程设计挑战的计算机辅助设计软件程序记录的。从日志文件中提取证据，并将其输入贝叶斯网络，以进行推理，并以后验概率的形式提供其性能的晴雨表。结果表明，贝叶斯网络模型能够较好地预测学生的任务绩效。它在识别特定群体的学生(回忆)和确保识别出的学生被正确标记(准确)两方面都做得很好。这项研究还表明，在将相关科学知识应用于工程设计任务方面，贝叶斯网络可以用来找出学生的优势和劣势。未来在计算机辅助设计软件中实施这一工具的工作将为教师提供一个强大的工具，通过实时自动生成对学生的个性化反馈来促进工程设计。
Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide personalized feedback on a timely basis. This study proposes a Bayesian network model to dynamically and automatically assess students’ engagement with engineering design tasks and to support formative feedback. Specifically, we applied a Bayesian network to 111 ninth-grade students’ process data logged by a computer-aided design software program that students used to solve an engineering design challenge. Evidence was extracted from the log files and fed into the Bayesian network to perform inferential reasoning and provide a barometer of their performance in the form of posterior probabilities. Results showed that the Bayesian network model was competent at predicting a student’s task performance. It performed well in both identifying students of a particular group (recall) and ensuring identified students were correctly labeled (precision). This study also suggests that Bayesian networks can be used to pinpoint a student’s strengths and weaknesses for applying relevant science knowledge to engineering design tasks. Future work of implementing this tool within the computer-aided design software will provide instructors a powerful tool to facilitate engineering design through automatically generating personalized feedback to students in real time.