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Title: Using Machine Learning Techniques to Capture Engineering Design Behaviors
Authors: Bywater, Jim P
Floryan, Mark
Chiu, Jennifer
Chao, Jie
Schimpf, Corey
Xie, Charles
Vieira, Camilo
Magana, Alejandra
Dasgupta, Chandan
Issue Date: Jul-2018
Publisher: International Society of the Learning Sciences, Inc. [ISLS].
Citation: Bywater, J. P., Floryan, M., Chiu, J., Chao, J., Schimpf, C., Xie, C., Vieira, C., Magana, A., & Dasgupta, C. (2018). Using Machine Learning Techniques to Capture Engineering Design Behaviors. In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 3. London, UK: International Society of the Learning Sciences.
Abstract: Engaging students in disciplinary practices can help students but many teachers face barriers implementing practice-based instruction as capturing, assessing, and providing feedback on practices can be labor and time intensive. This working paper reports on our early attempts to leverage machine learning techniques to analyze large process datasets of students engaged in engineering design projects within computer-aided environments. By identifying students’ engineering design behaviors, we eventually hope to examine how different sequences of these behaviors can be used to assess design practices with which we can provide intelligent feedback and guidance.
Appears in Collections:ICLS 2018

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