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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. 

Engineering projects, such as designing a solar farm that converts solar radiation shined on the Earth into electricity, engage students in addressing real-world challenges by learning and applying geoscience knowledge. To improve their designs, students benefit from frequent and informative feedback as they iterate. However, teacher attention may be limited or inadequate, both during COVID-19 and beyond. We present Aladdin, a web-based computer-aided design (CAD) platform for engineering design with a built-in artificial intelligence teaching assistant (AITA). We also present two curriculum units (Solar Energy Science and Solar Farm Design), where students explore the Sun-Earth relationship and optimize the energy output and yearly profit of a solar farm with the help of the AITA. We tested the software and curriculum units with over 100 students in two Midwestern high schools. Pre- and post-survey data showed improvements in understanding of science concepts and self-efficacy in engineering design. Pre-post analysis of design performance gains reveals that AI helped lower achievers more than higher achievers. Interviews revealed students’ values and preferences when receiving feedback. Our findings suggest that AITAs may be helpful as an additional feedback mechanism for geoscience and engineering education. Future efforts should focus on improving the usability of the software and providing multiple types of feedback to promote inclusive and equitable use of AI in education. 

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. 

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.