MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
Alvaro Becerra, Daniel Andres, Pablo Villegas, Roberto Daza, Ruth Cobos

TL;DR
MOSAIC-F is a comprehensive framework that combines multimodal data, AI, and human assessments to provide personalized feedback aimed at improving students' oral presentation skills.
Contribution
This paper introduces MOSAIC-F, a novel multimodal feedback framework integrating AI and human evaluations for personalized student learning insights.
Findings
Enhanced feedback accuracy through multimodal data integration
Improved student self-assessment via video review and AI insights
Potential for broader application in diverse learning activities
Abstract
In this article, we present a novel multimodal feedback framework called MOSAIC-F, an acronym for a data-driven Framework that integrates Multimodal Learning Analytics (MMLA), Observations, Sensors, Artificial Intelligence (AI), and Collaborative assessments for generating personalized feedback on student learning activities. This framework consists of four key steps. First, peers and professors' assessments are conducted through standardized rubrics (that include both quantitative and qualitative evaluations). Second, multimodal data are collected during learning activities, including video recordings, audio capture, gaze tracking, physiological signals (heart rate, motion data), and behavioral interactions. Third, personalized feedback is generated using AI, synthesizing human-based evaluations and data-based multimodal insights such as posture, speech patterns, stress levels, and…
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Taxonomy
TopicsVisual and Cognitive Learning Processes · Intelligent Tutoring Systems and Adaptive Learning · Emotion and Mood Recognition
