Expert distribution similarity model: Feedback methodology for non-imitation based handwriting practice

Olivier Dikken, Bibeg Limbu, Marcus Specht

Research output: Contribution to journalConference articleScientificpeer-review

43 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)46-52
Number of pages7
JournalCEUR Workshop Proceedings
Volume2979
Publication statusPublished - 2021
Event1st International Workshop on Multimodal Immersive Learning Systems, MILeS 2021 - Virtual, Online, Italy
Duration: 20 Sept 202124 Sept 2021

Keywords

  • Calligraphy
  • Feedback
  • Psychomotor skills
  • Sensors

Fingerprint

Dive into the research topics of 'Expert distribution similarity model: Feedback methodology for non-imitation based handwriting practice'. Together they form a unique fingerprint.

Cite this