The paper proposes MOSAIC-F, a multimodal feedback pipeline for oral presentations that combines human rubrics, sensors, and large language models, but it contains no outcome data or evaluation.
M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards
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abstract
We present a demonstration of a web-based system called M2LADS ("System for Generating Multimodal Learning Analytics Dashboards"), designed to integrate, synchronize, visualize, and analyze multimodal data recorded during computer-based learning sessions with biosensors. This system presents a range of biometric and behavioral data on web-based dashboards, providing detailed insights into various physiological and activity-based metrics. The multimodal data visualized include electroencephalogram (EEG) data for assessing attention and brain activity, heart rate metrics, eye-tracking data to measure visual attention, webcam video recordings, and activity logs of the monitored tasks. M2LADS aims to assist data scientists in two key ways: (1) by providing a comprehensive view of participants' experiences, displaying all data categorized by the activities in which participants are engaged, and (2) by synchronizing all biosignals and videos, facilitating easier data relabeling if any activity information contains errors.
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MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
The paper proposes MOSAIC-F, a multimodal feedback pipeline for oral presentations that combines human rubrics, sensors, and large language models, but it contains no outcome data or evaluation.