Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

Meishu Song, Kun Qian, Bin Chen, Keiju Okabayashi, Emilia Parada-Cabaleiro, Zijiang Yang, Shuo Liu, Kazumasa Togami, Ichiro Hidaka, Yueheng Wang, Bjoern Schuller, Yoshiharu Yamamoto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Embodied cognition theory states that students thinking in a learning environment is embodied in physical activity. In this regard, recent research has shown that signal-level handwriting dynamics can distinguish learning performance. Although machine learning has been considered to detect how multimodal modalities correlate to specific learning processes, the use of deep learning has received insufficient attention. With this in mind, we build a Group Work Performance Prediction system from analysis of 3D (including strokes frequency) handwriting signals of students in a smart English classroom, with deep convolutional neuronal network (CNN) based regression models. For labelling of their proficiency level, their spoken language performance is being used. The students were working together in groups. A 3D (2D writing coordinates plus frequency) handwriting dataset (3D-Writing-DB) was collected through a collaboration platform known as g creative digital space'. We extracted the 3D handwriting signal from a table tablet during English discussion sessions. Afterwards, professional English teachers annotated the English speech (values vary from 0 - 5). Our experimental results indicate that group work performance can be successfully predicted from physical handwriting features by using deep learning, as shown by our best result, i. e., 0.32 in regression assessment by applying RMSE for evaluation.

Original languageEnglish
Title of host publication2021 5th International Conference on Digital Signal Processing, ICDSP 2021
PublisherAssociation for Computing Machinery
Pages140-145
Number of pages6
ISBN (Electronic)9781450389365
DOIs
StatePublished - 26 Feb 2021
Externally publishedYes
Event5th International Conference on Digital Signal Processing, ICDSP 2021 - Virtual, Online, China
Duration: 26 Feb 202128 Feb 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Digital Signal Processing, ICDSP 2021
Country/TerritoryChina
CityVirtual, Online
Period26/02/2128/02/21

Keywords

  • English speaking proficiency
  • deep learning 11Dr. Kun Qian is the corresponding author.
  • digital classroom
  • group work
  • handwriting

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