2019 ASEE Annual Conference & Exposition

By graduation, engineering students are expected to learn engineering concepts from and solve problems with several types of visual representations (e.g., free-body diagrams, graphs, or schematics). Prior studies in engineering education have shown that students struggle to do both. Researchers in cognitive science suggest that learning from representations is an iterative process that depends on students’ prior knowledge, their goals, and what the features in a representation look like. However, these studies have not yet studied situations in which students modify the initial representation or handle multiple representations during a problem-solving task. Additionally, while researchers have studied what types of representations give better performance, less is known about why students perform better with certain types of representations. Under this NSF-EEC grant, we filled this gap in the literature by investigating the interplay between features that students notice in a representation, how they express their conceptual knowledge and the way they approach solving problems. We studied this interplay by conducting think-aloud interviews with students from two courses that represent two engineering disciplines that use multiple visual representations: statics and digital logic. We analyzed these interviews using the constant comparative method, which resulted in three emergent themes that describe ways that features of representations potentially hinder students’ ability to learn and use engineering concepts. Two of these were present in both datasets while one was only present in the statics dataset. We expanded upon this work by conducting a classroom intervention in the university’s large-enrollment statics course to test the generalizability of our findings. This paper will highlight the major findings from the cross-disciplinary analysis and discuss future research directions.