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Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings. [ABSTRACT FROM AUTHOR]

First-year engineering students are often introduced to the engineering design process through project-based learning situated in a concrete design context. Design contexts like mechanical engineering are commonly used, but students and teachers may need more options. In this article, we show how sustainable building design can serve as an alternative for students of diverse backgrounds and with various interests. The proposed Net Zero Energy Challenge is an engineering design project in which students practice the full engineering design cycle to create a virtual house that generates renewable energy on-site, with the goal to achieve net zero energy consumption. Such a design challenge is made possible by Aladdin, an integrated tool that supports building design, simulation, and analysis within a single package. A pilot study of the Net Zero Energy Challenge at a university in Mid-Atlantic United States suggests that around half of the students were able to achieve the design goal. [ABSTRACT FROM AUTHOR]

As solar energy becomes increasingly affordable, many schools are considering installing new solar power systems. In this article, the authors present Solarize Your School, an engineering project that gives students the opportunity to design and evaluate solar power solutions for their own schools. This STEM project requires students to learn and apply skills and practices related to solar energy and photovoltaic technology concepts, such as architectural measurement and modeling techniques, graphical interpretation and data analysis, budgeting and investing, scientific inquiry and engineering design, and collaboration and communication. Solarize Your School can be incorporated into environmental science, physical science, and engineering courses, and can be adapted to fit any curriculum scope and time frame. The authors suggest a 10-day sequence of learning activities.

Background: Design and science inquiry are intertwined during engineering practice. In this study, we examined the relationship between design behaviors and scientific explanations. Data on student design processes were collected as students engaged in a project on designing energy-efficient buildings on a blank square city block surrounded by existing buildings using a computer-aided design program, Energy3D, with built-in solar energy simulation capabilities. We used criterion sampling to select two highly reflective students among 63 high school students. Results: The main data sources were design replays (automatic playback of student design sequences within the CAD software) and electronic notes taken by the students. We identified evidence of informed design such as problem framing, idea fluency, and balancing benefits and trade-offs. Opportunities for meaningful science learning through engineering design occurred when students attempted to balance design benefits and trade-offs. Conclusions: The results suggest that design projects used in classrooms should emphasize trade-off analysis and include time and resources for supporting trade-off decisions through experimentation and reflection. Future research should explore ways to visualize patterns of design behavior based on large samples of students.

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structureaware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models' performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models' predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs. [ABSTRACT FROM AUTHOR]