Simulation-based learning provides students with unique opportunities to develop key procedural and teamwork skills in close-to-authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the…

Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the…

Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…

The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on…

This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design…