Technologies for automated analysis of co-located, real-life, physical learning spaces
Author:
Affiliation:
1. Nanyang Technological University, Singapore
2. National Institute of Education, Singapore
Publisher
ACM
Reference77 articles.
1. Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment
2. Using Multimodal Learning Analytics to Model Student Behavior: A Systematic Analysis of Epistemological Framing
3. A physiological index for situational interest in classroom settings using EEG (preliminary result)
4. Brain-to-Brain Synchrony and Learning Outcomes Vary by Student–Teacher Dynamics: Evidence from a Real-world Classroom Electroencephalography Study
5. A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis
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1. Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills;Education Sciences;2024-08-14
2. Evidence‐based multimodal learning analytics for feedback and reflection in collaborative learning;British Journal of Educational Technology;2024-06-22
3. The Impact of Attribute Noise on the Automated Estimation of Collaboration Quality Using Multimodal Learning Analytics in Authentic Classrooms;Journal of Learning Analytics;2024-06-20
4. Learning Analytics with Small Datasets—State of the Art and Beyond;Education Sciences;2024-06-05
5. Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analytics;British Journal of Educational Technology;2024-05-30
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