Visual models for abstract concepts towards better learning outcomes and self-efficacy

Abstract

We constructed and analyzed an evidence-based practice case to see if visual models help students develop a better understanding of abstract concepts and enhance their self-efficacy when solving engineering problems. Abstract concepts without corresponding physical phenomena are often found in the domains of industrial engineering, engineering management, and systems engineering. In this study, we focus on inventory control of a supply chain, which is typically a junior level undergraduate production systems course in an industrial engineering program. Visual models of inventory behaviors were designed to complement the traditional approach of mathematical derivations and numerical computations. In this context, we use a randomized-controlled design research framework implementing the visual models in a quiz. Pre- And post-surveys on student self-efficacy were used to assess the effects of the visual models. Students' quiz outcomes and self-efficacy surveys are compared to those from a control group that did not use the visual models, and the results from both groups were statistically analyzed. This study is motivated by engineering students' inability to understand abstract concepts and the need for continuous improvement of student learning. The results show that, within the scope of the aforementioned experiment and collected data, the visual models do help students understand abstract concepts and improve their self-efficacy. This study can serve as a basis for further studies on the extent of visual models helping students develop a complete mental model and on whether better mental models actually lead to a better understanding of the domain knowledge and enhance students' self-efficacy. © American Society for Engineering Education, 2014.