Sketches and other forms of graphical communication are central to both the practice and learning of engineering. Visual representations play a critical role in helping students learn engineering concepts, socialize them into the engineering discipline, and facilitate or hinder the design process. To help students practice and use these representation, several engineering colleges and classrooms have adopted tablet- and sketch-based instruction. Despite the sustained interest in sketching on tablets and the importance of graphical communication and visual representations, our understanding of how students learn these representations and use them is poor.
To better understand how students use and produce engineering sketches, we are conducting a series of novice-expert comparison studies. Investigations into the differences between experts and novices in their ability to process and recall information have provided a critical foundation in understanding how people learn. Knowledge of these differences have led to the creation of foundational educational theories (e.g., ontological shifts), assessment tools (e.g., concept inventories), and research-based instructional practices suitable for the classroom (e.g., bridging analogies) or computer-automated environments (e.g., hierarchical analysis tools). When studying how people learn about and use scientific diagrams, these studies have revealed that experts and novices find different elements of diagrams salient and that they chunk visual information differently. For example, experts emphasize underlying processes and functions in diagrams (e.g., focusing on the cycle of evaporation and rain illustrated in a diagram) while novices focus on surface features (e.g., focusing on the fact that there are clouds, lakes, and the sun in that same diagram). Critically, these studies have established that domain knowledge dramatically influences perception and understanding of visual representations.
While this project intends to explore expert-novice differences across a number of engineering disciplines, we have started by exploring these differences in how students and professors solve finite state machine problems from digital logic courses. We have interviewed 27 students and 6 faculty, recording their sketches and their verbal explanations as they solve canonical and open-ended design problems. Analysis of these interviews using a constant comparative method has revealed that sketching behaviors can reveal underlying conceptual knowledge and structures. For example, novices who lack conceptual understanding analyze and draw diagrams from top-left to bottom-right, while individuals with deeper conceptual understanding analyze and draw diagrams with non-linear patterns. In this paper, we will share more about differences that we have discovered and provide recommendations for instruction.
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