Practices of algorithm education based on discovery learning using a program visualization system

In this paper, we describe three practical exercises relating to algorithm education. The exercises are based on a learning support system that offers visualization of program behavior. Systems with the ability to visualize program behavior are effective to promote the understanding of algorithm behavior. The introduction of these systems into an algorithm course is expected to allow learners to cultivate a more thorough understanding. However, almost all existing systems cannot incorporate the teacher's intent of instruction that may be necessary to accommodate learners with different abilities by using a different instructional approach. Based on these considerations, we conducted classroom practice sessions as part of an algorithm course by incorporating the visualization system we developed in our previous work. Our system visualizes the target domain world according to the visualization policy defined by the teacher. Our aim with the practical classes is to enable learners to understand the properties of algorithms, such as the number of comparisons and data exchanges. The contents of the course are structured such that the properties of an algorithm can be understood by discovery learning in the practical work. In this paper, we provide an overview of our educational practices and learners' responses and show that the framework we use in our practices can be established in algorithm classes. Furthermore, we summarize the requirements for the inclusion of discovery learning in the algorithm classes as the knowledge obtained from our practices.