Abstract
This study develops a set of indicators for the quantitative evaluation of drawing work in the study of drawing. In basic drawing classes, students are taught to simplify the strokes that they use. To simplify a stroke means to use only straight lines and simple curves. In this study, we focus on the shapes of individual strokes in drawing work. We have been conducting a remote drawing learning support system at a Japanese art school since 2012. At this school, a digital drawing instruction using a digital pen is offered for 3 months at the beginning of each school year. Through drawing with a digital pen, each stroke that the learner applies is recorded together with the relevant temporal and geometrical information. In this paper, we describe the abstract stroke information collected and classify it using a self-organizing feature map, a machine learning method.
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Sugii, K., Nagai, T., Kayama, M. (2022). A Basic Study on Educational Growth Indicators Based on Quantitative Evaluation of Strokes Quality in Drawing Works. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_40
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