In programming education, refining code is crucial. Trial and error is one effective method for achieving this refinement. However, it can be challenging for learners to maintain motivation during conventional programming exercises due to them not knowing how good their code is and not being able to continuously perform trial and error. To address these issues, it is important to establish criteria for evaluating code quality and have learners objectively recognize them. Therefore, we propose and implement a method that uses a code visualization system that visualizes quality indicators, a ranking system, and a code viewing function based on that ranking. Evaluation of this system when implemented in a university classroom suggests that students write better code, based on these quality indicators, when they perform trial and error by themselves or by viewing other learners' code.

As many countries seek to promote competency-based education, formative assessments are important to capture the learning processes of learners. However, as yet there are no assessments that can fully capture the learning process. Recently, the use of ICT tools for learning has become more general, and learning log data has been accumulated. Using these data, it has become possible to capture learning processes in detail; therefore, data-driven assessment has attracted increasing attention. However, as conventional data-driven competency assessments require experts to map data to competencies, they can only be applied in a defined context. In this study, we proposed an assessment framework that allows teachers to assess their students’ competency by freely combining data collected as students used the Learning & Evidence Analytics Framework (LEAF) platform. We created an assessment in a scenario in an assumed educational setting using the proposed framework and examined what kind of assessment would be possible. Then, we created a system for the framework. Finally, interviews were conducted with three teachers regarding the system. The results suggest that the system can achieve context-independent and flexible data-driven assessment, contributing to the continuous improvement of learning and teaching from multiple perspectives in activities that use the system.

It is important to reuse knowledge acquired through problem-solving in programming. To reuse knowledge, it is effective to first understand differences between knowledge items and then to organize that knowledge. Therefore, we propose a method and develop a support system for facilitating knowledge organization in programming. We further examine a model of parts and problem-solving process of parts based on function–behavior– structure aspects. In this paper, we expand a previously developed system to propose an improved system that provides support based on feedback from these models. The aim of this system is to allow learners to consider behavior rather than thinking directly from function to structure.