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Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning. 

Practitioners and researchers in geoscience education embrace collaboration applying ICON (Integrated, Coordinated, Open science, and Networked) principles and approaches which have been used to create and share large collections of educational resources, to move forward collective priorities, and to foster peer‐learning among educators. These strategies can also support the advancement of coproduction between geoscientists and diverse communities. For this reason, many authors from the geoscience education community have co‐created three commentaries on the use and future of ICON in geoscience education. We envision that sharing our expertise with ICON practice will be useful to other geoscience communities seeking to strengthen collaboration. Geoscience education brings substantial expertise in social science research and its application to building individual and collective capacity to address earth sustainability and equity issues at local to global scales The geoscience education community has expanded its own ICON capacity through access to and use of shared resources and research findings, enhancing data sharing and publication, and leadership development. We prioritize continued use of ICON principles to develop effective and inclusive communities that increase equity in geoscience education and beyond, support leadership and full participation of systemically non‐dominant groups and enable global discussions and collaborations.