Analyzing Collaborative Learning Process by Deep Learning Methods: A Multi-Dimensional Coding Scheme with an Assessment Model
Analyzing Collaborative Learning Process by Deep Learning Methods: A Multi-Dimensional Coding Scheme with an Assessment Model
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发表时间：
2019
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通讯作者：
Taketoshi Inaba;Chihiro Shibata;Kimihiko Ando
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作者：
Taketoshi Inaba;Chihiro Shibata;Kimihiko Ando
-在计算机支持的协作学习研究中，通过分析交互激活的机制，提取用于区分协作过程中进展不佳的群体的指标，从而找出实施适当支架的指导方针，可能是一项非常重要的任务。对于这种协作过程分析，通常采用适当地表示每个贡献的属性的标签(编码)和统计分析作为方法。但就本文而言，它试图用深度学习技术来自动化这一巨大的繁琐的编码工作。在其先前的研究中，监督数据是基于根据言语行为的16个标签组成的编码方案来准备用于深度学习的。为了更全面、更多地分析协作学习过程，设计了一种新的五维多维编码方案，利用深度学习方法实现了自动编码，并验证了其准确性。结果表明，我们可以肯定地将该模型引入到真实的教育环境中，即使对于学生人数较多的大班，我们也可以对学习过程进行实时监控或对大数据进行事后分析。然而，在每个维度上呈现自动编码的原始结果并不足以向教师和学生表明协作过程的质量。为此，提出了一种新的能够评估和可视化协同过程质量的评分模型。
—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 real-time 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.