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Abstract. In this paper, we describe the development of a support system that facilitates the process of learning computer programming through the reading of computer program source code. Reading code consists of two steps: reading comprehension and meaning deduction. In this study, we developed a tool that supports the comprehension of a program's reading. The tool is equipped with an error visualization function that illustrates a learner's mistakes and makes them aware of their errors. We conducted experiments using the learning support tool and confirmed that the system is effective.

In science education, conventional problem practice hardly helps students reach “conceptual understanding” with which they can solve various problems by making appropriate models of target systems. Students often superficially read the solution of a problem and apply it wrongly to others without understanding the model. It is difficult to teach how to make appropriate models because model-making expertise includes a lot of implicit knowledge. In this paper, we propose a general framework for systematically describing such knowledge, which makes it possible not only to explain various models and the difference between them but also to design/sequence a set of problems appropriate for promoting conceptual understanding. Our framework was proved useful through a preliminary experiment in which the explanations generated based on our framework promoted subjects’ (15 graduates and undergraduates) conceptual understanding in mechanics. The framework can be the basis for designing intelligent tutoring systems which explicitly help students reach conceptual understanding.

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We investigate whether academic emotions are affected by the color of a robot's eyes in lecture behaviors. In conventional human-robot interaction research on robot lecturers, the emphasis has been on robots assisting or replacing human lecturers. We expanded these ideas and examined whether robots could lecture using one's behaviors that are impossible for humans. Psychological research has shown that color affects emotions. Because emotion is strongly related to learning, and a framework of emotion control is required. Thus, we considered whether emotions related to the learner's academic work, called "academic emotions," can be controlled by the color of a robot's illuminated eye light. In this paper, we found that the robot's eye light color affects academic emotions and that the effect can be manipulated and adapted to individuals. Furthermore, the manipulability of academic emotions by color was confirmed in a situation mimicking a real lecture.