Work

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

Year: 2021

Type: conference-paper

Source: IEEE International Conference on Digital Signal Processing (DSP)

DOI: 10.1145/3458380.3458404

Authors: 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

Institutions: University of Augsburg, The University of Tokyo, Fujitsu (Japan), Johannes Kepler University of Linz

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: closed

Funders: European Commission, Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science

Awards: Smart environments for person-centered sustainable work and well-being, Development of just-in-time adaptive intervention for behavioral modification based on continuous psycho-behavioral monitoring under daily life, G434337789, G5612655108, G576142307, 強縦断的な生体医学信号の深層学習と健康関連の人工知能応用, G7426172400

OpenAlex ID: W3202770082

FWCI: 0.508

Cites: 38

Cited by: 2

Related to: 9