Engineering and Computer Science Faculty Publications
Document Type
Conference Proceeding
Publication Date
8-23-2022
Journal Title
ASEE 129th Annual Conference
Abstract
One of the biggest challenges in teaching civil engineering students a theory-intensive course like structural analysis is helping students make the connection between the engineering mechanics taught at the front of the room and how those concepts define the real behavior of actual engineered structures. Absent this connection, students will often learn how to successfully perform the mathematical functions on their homework assignments but lack confidence in their ability to apply the same concepts to the analysis or design of an actual structure. Common ways to try to provide this real-world application of structural analysis principles include the use of small-scale physical models, often referred to as desktop learning modules (DLMs), software modeling, or case studies of full-scale structures. Each of these options possesses a significant limitation when it comes to helping students form cognitive connections: DLMs often lack adaptability or measurability, software helps provide visualization of engineering mechanics but lacks a connection to actual physical behavior, and full-scale structures are rarely able to be loaded to produce observable behavior. An ideal learning experience for students would include the synthesis of all of these tools to help students develop cognitive connections between mechanics principles, engineering design tools, and real-world structures through hands-on and problem-based learning.
A popular, recently developed, commercially available structural modeling DLM (Mola Structural Kit; no association with the authors) provides a high enough level of structural simulation and adaptability that it should allow for the kind of learning synthesis that has traditionally been challenging to produce. The Mola DLM permits students to create a variety of structural models that can reasonably approximate case studies of real structural behaviors in a manner that can be measured and compared to models developed using structural analysis software. The purpose of this study is to evaluate the effectiveness of an approach combining RISA 3D structural engineering software, the Mola physical model, and examples from actual structural systems at helping students form correct cognitive connections between principles of engineering mechanics and the behaviors of real structures.
Preparation for this study involved mechanically characterizing Mola components, developing parameters for implementation in structural analysis software, and validating the process of comparison between physical and computational models. Once the concept was confirmed to be practicable, worksheet-driven activities were developed and conducted in two undergraduate engineering classes. For these activities, students worked in small groups as they considered real-world applications of either portal frames or lateral force resisting systems, built Mola and structural analysis software models that reflected these real structural systems, then compared the modeled behaviors to practical applications of these concepts. Assessment was conducted via a mixed methods study using quantitative pre- and post-assessments and a small selection of follow-up interviews. Results suggest that students completing the activities demonstrated an increased ability to connect the concepts displayed by the physical models to the behaviors of the computational models and the applications in real-world structures. However, these gains did not seem to be uniform across all students, and modifications to the activity in future iterations may be able to further increase this and similar activities’ effectiveness.
Recommended Citation
Dittenber, David and Fredette, Luke Thomas, "Forming Congnitive Connections: Desktop Learning Modules, Structural Analysis Software, and Full-Scale Structures" (2022). Engineering and Computer Science Faculty Publications. 469.
https://digitalcommons.cedarville.edu/engineering_and_computer_science_publications/469
Additional Copyright
© 2022 American Society for Engineering Education
