Expert distribution similarity model
Feedback methodology for non-imitation based handwriting practice
Limbu Limbu (TU Delft - Web Information Systems, Leiden-Delft-Erasmus Centre for Education and Learning (LDE-CEL))
Marcus Specht (TU Delft - Web Information Systems, Leiden-Delft-Erasmus Centre for Education and Learning (LDE-CEL))
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Abstract
Learning fine psychomotor skills such as handwriting is a tedious endeavour which requires close supervision of the teacher to master. However, the increasing number of students in classes means less time a teacher can allocate for each student. This adversely affects the development of handwriting in students. Sensor-based technologies can help address this problem, as they are capable of providing feedback to the student whilst the teacher is not present during the student's writing. While there are multiple sensor-based applications to date for handwriting practice, such applications provide feedback in only for simple tracing over practice tasks. In this paper, we present a conceptual methodology using AI and sensors, for providing feedback in non-tracking tasks that do not have a single correct solution and allow larger variations.
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