In a pilot study conducted in December 2012, high school students in an engineering class used our Energy3D CAD tool to do an urban solar design project -- they must consider the sun path in four seasons and the existing buildings in the neighborhood as the design constraints to optimize solar penetration to the new buildings and minimize obstruction of sunlight to the existing buildings.
Energy3D can log any student actions and intermediate steps, which provide extremely detailed information about student design processes. With such a high-resolution lens, we could characterize student patterns and analyze how they solve the design challenge closely. For example, the CAD log allows us to reconstruct the entire design process of each student and show it in an unprecedentedly fine-grained timeline graph. A timeline graph may show how students went through different iterative steps while shaping their designs. For instance, did they consider the interactions among the buildings they designed? Did they go back to revise a previously erected building that may be affected by a newly added one? The timeline data we have collected show that the students' designs demonstrated more iterative features as they moved on to explore and design alternatives following the initial attempts (perhaps encouraged by the gained familiarity with and confidence in the CAD tool).
A design timeline (click to enlarge)
Our analyses also suggest that there appears to be a significant gender difference in both design products and processes. The main differences are: 1) The boys tended to push the limit of the software and produced unconventional designs that looked "cool" but did not necessarily meet the design specifications; and 2) The girls spent more time carefully revising their designs than building new structures. While these findings may not be surprising to some seasoned educators, the significance is that this may be the first time this kind of gender difference was revealed or confirmed by empirical data from CAD logs. Using CAD logs may provide a fairer basis of assessing student performance based on the entire learning process rather than just looking at their final products or self reports.
Summary of the results
The implication of this study is that if we can identify patterns in student design learning and understand their cognitive meanings, we could devise a software system that can provide real-time feedback to help students learn in the future. For example, could the software prompt students to consider the design criteria more when it detects that students are ignoring them? Could the software stimulate students to think out of the box more when it detects that students are underexploring the design space?