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ThinkMind // International Journal On Advances in Intelligent Systems, volume 10, numbers 3 and 4, 2017 // View article intsys_v10_n34_2017_15

Coding Collaboration Process Automatically: Coding Methods Using Deep Learning Technology

Authors:
Kimihiko Ando
Chihiro Shibata
Taketoshi Inaba

Keywords: CSCL; leaning analytics; coding scheme; deep learning methods.

Abstract:
In Computer Supported Collaborative Learning (CSCL) research, gaining a guideline to carry out appropriate scaffolding by analyzing mechanism of successful collaborative interaction and extracting indicators to identify groups where collaborative process is not going well, can be considered as the most important preoccupation, both for research and for educational implementation. And to study this collaborative learning process, different approaches have been tried. In this paper, we opt for the verbal data analysis; the advantage of this method is that it enables quantitative processing while maintaining qualitative perspective, with collaborative learning data of considerable size. However, coding large scale educational data is extremely time consuming and sometimes goes beyond men’s capacity. So, in recent years, there have also been attempts to automate complex coding by using machine learning technology. In this background, with large scale data generated in our CSCL system, we have tried to implement automation of high precision coding utilizing deep learning methods, which are derived from the leading edge technology of machine learning. The results indicate that our approach with deep learning methods is promising, outperforming the machine learning baseline. But the prediction accuracy could be improved by constructing coding schemes and models more sensitive to the context of collaboration and conversation. Therefore, we propose a new coding scheme that can represent the context of learning more comprehensively and accurately at the end of this paper for the next research.

Pages: 345 to 354

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: December 31, 2017

Published in: journal

ISSN: 1942-2679
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