Designing for Future Learning Environments through Multimodal Learning Analytics
The proliferation and ubiquity of computing technologies has opened a new range of technology application in educational research. Multimodal learning analytics are the product of the initiatives to understand learning in its full complexity and dimensionality, and to bridge the gap between teaching and learning across space, time, and media. Hence, learning is a multimodal complex process that links linguistic, visual, gestural, and actional resources. Consequently, the potential of improving learning and teaching lies in the capacity to capture multifaceted learner-generated data coming from various e-learning tools (e.g., clickstream data, assessment data, grades), and data collected with sensors (e.g., EEG data, gaze data), wearable devices (e.g., heart rate, temperature, skin conductance), cameras, and other computing devices. Finally, the synergy between learning analytics and learning design should be utilized in extracting measures from the multifaceted learner-generated data and interpreting those measures to inform existing learning theories for the purpose of developing actionable initiatives in teaching practices, as well as design principles and knowledge between data representation and data-driven actions.
About our speaker
Katerina Mangaroska is a PhD student at the Department of Computer Science at the Norwegian University of Science and Technology. Her primary research area is learning analytics, in particular, multimodal learning analytics. Currently she is engaged in the “Future Learning: Orchestrating 21st Century Learning Ecosystem using Analytics” project. The aim of the project is to develop new knowledge on how analytics allow us to better orchestrate different learning tools and practices, and design optimal learning environments. Most of her research is centred in the context of learning programming utilizing various modalities (e.g., gazing, gesturing, facial expressions, and some unintentional utterances such as blood pressure, temperature, heartbeat). Her other research interests centre around learning design, intelligent tutoring systems, learning infrastructure for computing education, human-computer interaction, and cognition-aware systems. Mangaroska is a Fulbright scholar.