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Authors:
Takahiro Kanayama
Kimihiko Ando
Chihiro Shibata
Taketoshi Inaba
Keywords: CSCL; coding scheme; deep learning methods, automatic coding
Abstract:
In computer-supported collaborative learning research, it may be a significantly important task to figure out guidelines for carrying out an appropriate scaffolding by extracting indicators for distinguishing groups with poor progress in collaborative process upon analyzing the mechanism of interactive activation. And for this collaborative process analysis, coding and statistical analysis are often adopted as a method. But as far as our project is concerned, we are trying to automate this huge laborious coding work with deep learning technology. In our previous research, supervised data was prepared for deep learning based on a coding scheme consisting of 16 labels according to speech acts. In this paper, with a multi-dimensional coding scheme with five dimensions newly designed aiming at analyzing collaborative learning process more comprehensively and multilaterally, an automatic coding is performed by deep learning methods and its accuracy is verified.
Pages: 45 to 53
Copyright: Copyright (c) IARIA, 2018
Publication date: March 25, 2018
Published in: conference
ISSN: 2308-4367
ISBN: 978-1-61208-619-4
Location: Rome, Italy
Dates: from March 25, 2018 to March 29, 2018