Evaluation of Automatic Coding for Collaborative Learning Process Based on Multi-Dimensional Coding Scheme

DOI

Bibliographic Information

Other Title
  • 多次元コーディングスキームに依拠した協調学習プロセスの 自動コーディングの精度検証

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. In addition, we apply our methods to predict another dataset for verification and investigate the correlation between the multidimensional coding labels and the assessments given by professionals manually.

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Details 詳細情報について

  • CRID
    1390852870557648384
  • NII Article ID
    130008108152
  • DOI
    10.51034/jasla.2.0_11
  • ISSN
    24366862
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Allowed

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