Design a city block with Energy3D.

We were in a school these two weeks doing a project that aims to understand how students learn engineering design. This has been a difficult research topic as engineering design is an extremely complicated cognitive process that involves the application of science and mathematics — another two sets of complicated subjects themselves.

Two types of problems are commonly encountered in the classroom. The first type is related to using a “cookbook” approach that confines students to step-by-step procedures to complete a “design” project. I added double quotes because this kind of project often leads to identical or similar products from students, violating the first principle of design that mandates alternatives and varieties. However, if we make the design project completely open-ended, we will run into the second type of problem: The arbitrariness and caprice in student designs often make it difficult for teachers and researchers to assess student thinking and learning reliably. As much as we want students to be creative and open-minded, we also want to ensure that they learn what is intended and we must provide an objective way to evaluate their learning outcomes.

To tackle these issues, we are taking a computer science-based approach. Computer-aided design (CAD) tools offer an opportunity for us to move the entire process of engineering design to the computer (this is what CAD tools are designed for in the first place for industry folks). What we need to do in our research is to add a few more things to support data mining.

A sample design of the city block.

This blog post reports a timeline tool that we have developed to measure student activity levels while engaged in using a CAD tool (our Energy3D CAD software in this case) to solve a design challenge. This timeline tool is basically a logger that records the number of the learner’s design actions at a given frequency (say, 2-4 times a minute) during a design session. These design actions are defined to be the “atomic” actions stored in the Undo Manager of the CAD tool we are using. The timeline approximately describes the user’s frequency of construction actions with the CAD tool. As the human-computer interaction is ultimately driven by the brain, this kind of timeline data could be regarded as a reflection of the user’s “brain wave.”

There are four things that characterize such a timeline graph:

A sample timeline graph.

• The height of a spike measures the action intensity at that moment, i.e., how many actions the user has taken since the last recording;
• The density of spikes measures the continuity and persistence of actions over a time period;
• A gap indicates an off-task time window: A short idling window may be an effect of instruction or discussion;
• The trend of height and density may be related to loss of interest or improvement of proficiency in the CAD tool: If the intensity (the combination of height and density of spikes) drops consistently over time, the student’s interest may be fading away; if the intensity increases consistently over time, the student might be improving on using the design tool to explore design options.

Timeline graphs from six students.

Of course, this kind of timeline data is not perfect. It certainly has many limitations in measuring learning. We are still in the process of analyzing these timeline data and juxtaposing them with other artifacts we have gathered from the students to provide a more comprehensive picture of design learning. But the timeline analysis represents a rudimentary step towards a more rigorous methodology for performance assessment of engineering design.

The above six “brain wave” graphs were collected from six students in a 90-minute class period. Hopefully, these data will lead to a way to identify novice designers’ behaviors and patterns when they are solving a design challenge.