Dancing hamsters and marble statues: characterizing student visualizations of algorithms

Algorithm visualization research for computer science education has primarily focused on expert-created visualizations. However, constructionist and situated theories of learning suggest that students should develop and share their own diverse understandings of a concept for deep learning. This paper presents a novel approach to algorithm learning by visualization construction, sharing, and evaluation. Three empirical studies in which students engaged in these activities are discussed. The resulting learning benefits are quantified, and student visualizations are characterized in multiple ways. Then another study that investigated how specific characteristics of such visualizations influence learning is described. This work demonstrates the effectiveness of having students create algorithm visualizations, identifies characteristics of student-created algorithm visualizations and illuminates the learning benefits derived from these characteristics.

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