Developing a Multimodal Learning Analytics Approach for Collaborative Learning and Metacognitive Strategies in Virtual Learning Environments for Primary Science Education
DOI:
https://doi.org/10.58459/icce.2024.5050Abstract
The growing use of virtual learning environments (VLEs) in primary education offers new opportunities for enhancing student learning. However, understanding and analysing student behaviour in these environments is still challenging, especially in collaborative science education. This research aims to develop and evaluate a multimodal learning analytics (MMLA) approach tailored for primary science education. The study will focus on four key questions: understanding the current state of learning analytics (LA) in VLEs, identifying which data types in MMLA most effectively contribute to insights into student learning behaviours, creating an MMLA approach to support collaborative problem solving (CPS) and metacognitive strategies in VLEs, and improving the visualization of MMLA results for educators and students. To achieve these goals, the research will use a series of studies that collect and analyse multimodal data, including eye-tracking, behaviour logs, and dialogue text. Machine learning and deep learning techniques will be applied to identify critical data types, and these insights will inform the creation of an MMLA approach specifically designed to support CPS and metacognitive strategies. A user-centred design will guide the creation of a visualisation dashboard. This research is expected to contribute to theory by expanding the application of the MMLA approach, critically reflecting on the potential to innovate CPS and metacognitive strategies within VLEs and improving data analysis and dashboard design, and to practice by enhancing tools for primary science education.