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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. 

Abstract In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available. 

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels. Although the classroom orchestration of the particular learning design was customised for specific audiences and contexts, findings from this study suggest that the core components of the learning design, such as content, assessment, and pedagogy, and their alignment among them, resulted in students’ learning. Specifically, results from a pre-post science assessment suggest that the three student groups arrived at similar understanding post-intervention levels, along with a significant aggregate growth in their scientific understanding. Regarding design performance, students in different groups demonstrated different levels of success in meeting design constraints. The findings also suggest that students’ success rate in meeting the design constraints directly influenced their final design performance, where middle-school students had better performance than students in the other groups. That is, across the board, students increased their conceptual understanding of heat transfer, Earth, and solar science and were able to produce feasible designs. Implications of the study include how learning experiences with engineering and science simulations should be designed so that teachers can adopt and adapt materials for their specific audiences, contexts, and settings. 

Understanding the design process may reveal when and where resources should be focused and how engineers can better use tools, methods, and techniques to enhance the quality of designs and creative performance. The literature suggests the importance of iterative evaluation and reformulation in the engineering design process. The current study collected 111 high school students’ logs of designing an energy-saving house in Energy3D, a computer-aided design environment. Using a cross-lag model, we investigated the reciprocal relationship between students’ evaluation and reformulation behaviors and how these behaviors influence students’ design performance at the early, middle, and final design stages. The results suggest that there is a positive predictive relationship between students’ evaluation and reformulation process; reformulation positively predicts design performance and mediates the relationship between evaluation and design performance across time. These results provide empirical evidence of the importance of iterative evaluation and reformulation in the design process and implications for teachers and system designers to support students’ design.