Recent studies have emphasized the importance of formative assessments in improving teaching and learning. This study investigated what insights can be gained from analyzing students' pen-stroke data in homework to support formative assessment (n=37). The results showed that pen-stroke data revealed students' thought processes, including partial understanding and trial-and-error attempts, which were not visible in the final answers. Regarding this result, one mathematics teacher interviewed expressed an interest in using pen-stroke data in the classroom, particularly in specific units. This study concluded that pen-stroke data can enhance formative assessment by offering more profound insights into student learning.

Handwriting logs in the math answering process have recently been collected, and features related to the answering performance and the process, such as the stroke duration, have been investigated. However, the results reported in previous studies showed inconsistencies, and sufficient consideration had not been given to the differences in the problem types. In this study, we classified some problems into two types and verified whether there is a difference in the effect of the handwriting process on performance in each feature. The result of the analysis showed a significant difference in the effects of problems on the features used in this study, such as answering time and number of strokes. This study contributes to the need to take into consideration the problem type in learning support with handwriting process logs.

We extracted learners' characteristic answering behaviors from handwritten process log data and investigated whether learners' situations could be inferred based on these characteristics. The result showed we were able to extract several characteristic answering behaviors, such as stopped pen stroke and late start. Furthermore, we examined the learners' situation for each feature in the actual answering process. The results revealed that several characteristic answering behaviors indicated situations such as learners' stumbling or giving up. These results imply that handwritten process log data can allow teachers to capture learners' situations and support teachers' interventions.

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.