This research began by investigating the literature on student learning from algorithm animations and conducting experimental studies of an algorithm visualization system. The results led us to develop CAROUSEL (Collaborative Algorithm Representations Of Undergraduates for Self-Enhanced Learning), using which students created expository representations of algorithms, shared their representations with others, evaluated each other's representations and discussed them. The system and the activities of representation creation, sharing, evaluation and discussion that it supports were then studied in three experiments, which are summarized. They show a significant positive relationship between these constructive and collaborative activities and algorithm learning, which suggests that this is a beneficial pedagogical approach for introductory courses on 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.