This paper proposes a novel computational approach based on time series analysis to assess engineering design processes using a CAD tool. To collect research data without disrupting a design learning process, design actions and artifacts are continuously logged as time series by the CAD tool behind the scenes, while students are working on a design challenge. These fine-grained data can be used to reconstruct and analyze the entire design process of a student with extremely high resolution. Results of a pilot study in a high school engineering class, in which students solved a solar urban design challenge, suggest that these data can be used to measure the level of student engagement, reveal the gender differences in design behaviors, and distinguish the iterative and non-iterative cycles in a design process. From the perspective of engineering education, this paper contributes to the emerging fields of educational data mining and learning analytics that aim to expand evidence approaches for learning in a digital world.
