Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning

Abstract

Collaborative learning activities are a key part of education and are part of many
\ncommon teaching approaches including problem-based learning, inquiry-based learning, and
\nproject-based learning. However, in open-ended collaborative small group work where
\nlearners make unique solutions to tasks that involve robotics, electronics, programming, and
\ndesign artefacts evidence on the effectiveness of using these learning activities are hard to
\nfind. The paper argues that multimodal learning analytics (MMLA) can offer novel methods
\nthat can generate unique information about what happens when students are engaged in
\ncollaborative, project-based learning activities. Through the use of multimodal learning
\nanalytics platform, we collected various streams of data, processed and extracted multimodal
\ninteractions to answer the following question: which features of MMLA are good predictors
\nof collaborative problem-solving in open-ended tasks in project-based learning? Manual
\nentered scores of CPS were regressed using machine-learning methods. The answer to the
\nquestion provides potential ways to automatically identify aspects of collaboration in projectbased
\nlearning.