An AI-enhanced Pattern Recognition Approach to Analyze Children's Embodied Interactions

Ceren Ocak, Theodore J. Kopcha, Raunak Dey

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

This study first presents an approach to the study of computational thinking (CT) as an embodied phenomenon that relies on the creation and analysis of multimodal transcription transcripts. The approach, which incorporates a social semiotic approach to multimodality, is then used to train an artificial intelligence (AI) to recognize patterns in the participant's behaviors that reflect their embodiment of CT during an educational robotics activity. The AI was developed to ease the labor-intensive aspects of creating and analyzing a multimodal transcript. The findings suggested that the AI-enhanced pattern recognition approach identified similar clusters of activity as human analysis, adding a level of confidence to the analysis of children's CT that would be difficult to achieve using human analysis.

Original languageEnglish
Title of host publication29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
EditorsMaria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
PublisherAsia-Pacific Society for Computers in Education
Pages273-278
Number of pages6
ISBN (Electronic)9789869721486
StatePublished - Nov 22 2021
Event29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
Duration: Nov 22 2021Nov 26 2021

Publication series

Name29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
Volume2

Conference

Conference29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period11/22/2111/26/21

Keywords

  • Artificial intelligence (AI)
  • Computational thinking (CT)
  • Embodied interactions
  • Social semiotic approach to multimodality

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