アンドウ　キミヒコ   安藤　公彦    所属   先進教育支援センター 教員    職種   専任講師
言語種別	英語
発行・発表の年月	2018/12
形態種別	学術論文
査読	査読あり
標題	Analyzing Collaborative Learning Process by Deep Learning Methods: A Multi-Dimensional Coding Scheme with an Assessment Model
執筆形態	共著
掲載誌名	International Journal On Advances in Software
掲載区分	国外
出版社・発行元	IARIA
巻・号・頁	11(3&4),pp.335-346
総ページ数	12
著者・共著者	Taketoshi Inaba, Chihiro Shibata, Kimihiko Ando
概要	This article is an extended version of a conference paper presented at eLmL 2018. 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, labelling for appropriately representing properties of each contribution (coding) and statistical analysis are often adopted as a method. But as far as this paper is concerned, it tries to automate this huge laborious coding work with deep learning technology. In its 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. The results indicate with certainty that we can introduce this model to authentic educational settings and that even for large classes with many students, we can perform realtime monitoring of learning process or ex-post analysis of big educational data. However, presenting raw results of automatic coding on each dimension is not enough to indicate the collaborative process quality to teachers and students. Therefore, a new rating model that can assess and visualize the quality of collaborative process is proposed.