Exploring Expert and Novice Graphical Communication Through Digital Sketching

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The ability to communicate through graphical means such as sketching is central to engineering education and practice, yet little is known about how, why, and when students use engineering sketches or how they learn to use them. This project studies how novices and experts produce and respond to their own sketches, and builds on existing techniques for digital sketch recognition. Students' representational fluency will be examined by exploring the similarities and distinctions between experts and novices in their use and production of engineering sketches during problem solving by focusing on sketching in two domains: digital logic and trusses. These domains use complementary styles of sketches as digital logic sketches are visually distinct from real-world implementations, and trusses rely on facsimiles of real-world objects. The use of complementary domains will provide richer observations that can inform the future development of theory and pedagogical tools. Educational data mining of students' sketches will provide new observations on sketching behaviors (e.g., pen speed, pressure) that might be obscured or invisible to human researchers. Think-aloud interviews will be conducted as students and graduate teaching assistants solve analysis and design problems for digital logic and trusses. Data from these interviews will be analyzed with qualitative analysis techniques to categorize behaviors of novices and experts. Additionally, preliminary data mining will be performed on digital pen inputs to identify salient variables that can be detected algorithmically. The intellectual merit of this project is established by its basis on a rich tradition in the study of expert-novice differences. This project is particularly informed by prior research on how novices and experts comprehend and use static images in their reasoning. Aligning educational data mining with qualitative analysis of sketching will inform the development of a new form of mixed methods research, advancing the state of the art in educational research methods. The broader impacts of this project will be seen through three different facets: i) providing new avenues for richer credentialing in engineering education, ii) enhancing and maximizing the effectiveness of pedagogies and their tools through data mining, and iii) producing a large corpus of sketching, simultaneously building a developer/user community that can use that database.