Characterization of Auxiliary Problems for Automated Generation in Error-Based Simulation

Abstract

Error-based Simulation (EBS) is a learning support framework that visualizes learner’s errors. Learning with EBS encourages trial and error, unlike learning by instruction, because learners are required to actively challenge problems. However, when problems are complex and difficult, learners may get stuck. In such cases, it is effective to present auxiliary problems that can support learning of the original problem. Learning with auxiliary problems requires learners to actively challenge problems compared with more passive learning by instruction. However, the creation of many auxiliary problems places a burden on teachers, especially because there are no rules to creating these problems. Therefore, we considered that a system that automatically generates auxiliary problems according to rules would provide the required auxiliary problems. Such a system would lift the burden on teachers who would otherwise need to create the auxiliary problems. In this paper, we discuss characterization and generation rules for the purpose of automated generation of auxiliary problems in EBS. We propose three types of operations that generate auxiliary problems: replace, balanced delete, and simple delete. Using these operations to generate auxiliary problems in a trial, we generated fourteen auxiliary problems.