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This study explored the implementation of a novel approach to dual credit referred to as the facilitator model that can be suited for STEM-focused coursework such as courses focused on engineering, design, technology, and innovation. Unlike other models, high school teachers facilitate the implementation of a college course for both high school and college credit in collaboration with a university instructor who evaluates student learning. This novel approach was specifically implemented for an open-ended undergraduate design course within an engineering technology college, similar to many first-year engineering course experiences that emphasize project based learning, from a large research-intensive public university. For this study, the facilitator model was piloted with five high school teachers as facilitators of an undergraduate design course for dual credit at two innovative, STEM-focused public charter schools. The qualitative research design focused on examining (1) teacher needs while implementing, and perceptions of, the dual credit facilitator model for an undergraduate design course in urban public charter schools and (2) the impact of this model on student learning. This study included the collection and analysis of over 90 h of interviews, focus groups, surveys, and observations. Results provide a promising outlook for the use of the facilitator model when delivering dual credit content that is open ended and within the context of design, technology, and engineering by (1) navigating multiple institutional policies and processes related to dual-credit implementation, (2) providing ongoing support and fostering collaboration between high schools and university, (3) enabling students to earn directly transcripted college credits that count as a required course toward degree completion, and (4) increasing affordability and access to dual credit coursework. These potential advantages over other dual credit models can help address barriers that may limit access to dual credit coursework, specifically for underserved high schools. 

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.