Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning
Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning
复制标题
使用多模式学习分析来确定基于项目的学习中的协作方面
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Cukurova
中科院分区：
文献类型：
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作者：
Daniel Spikol;E. Ruffaldi;M. Cukurova
协作学习活动是教育的一个关键部分，也是许多活动的一部分
Collaborative learning activities are a key part of education and are part of many
common teaching approaches including problem-based learning, inquiry-based learning, and
project-based learning. However, in open-ended collaborative small group work where
learners make unique solutions to tasks that involve robotics, electronics, programming, and
design artefacts evidence on the effectiveness of using these learning activities are hard to
find. The paper argues that multimodal learning analytics (MMLA) can offer novel methods
that can generate unique information about what happens when students are engaged in
collaborative, project-based learning activities. Through the use of multimodal learning
analytics platform, we collected various streams of data, processed and extracted multimodal
interactions to answer the following question: which features of MMLA are good predictors
of collaborative problem-solving in open-ended tasks in project-based learning? Manual
entered scores of CPS were regressed using machine-learning methods. The answer to the
question provides potential ways to automatically identify aspects of collaboration in projectbased
learning.