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Towards Automatic Coding of Collaborative Learning Data with Deep Learning Technology

Authors:
Chihiro Shibata
Kimihiko Ando
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; its 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 baselines, and that the prediction accuracy could be improved by constructing models more sensitive to the context of conversation.

Pages: 65 to 71

Copyright: Copyright (c) IARIA, 2017

Publication date: March 19, 2017

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-541-8

Location: Nice, France

Dates: from March 19, 2017 to March 23, 2017