# MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback

arXiv: https://arxiv.org/abs/2506.08634 · HTML: https://mlsanity.com/paper/2506.08634/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education](https://mlsanity.com/paper/2305.12561/) (2023)
- [VAAD: Visual Attention Analysis Dashboard applied to e-Learning](https://mlsanity.com/paper/2405.20091/) (2024)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Co-Attention Based Neural Network for Source-Dependent Essay Scoring](https://mlsanity.com/paper/1908.01993/) (2019)
- [SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality](https://mlsanity.com/paper/2501.10977/) (2025)
- [LLM-Driven Learning Analytics Dashboard for Teachers in EFL Writing Education](https://mlsanity.com/paper/2410.15025/) (2024)
- [M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards](https://mlsanity.com/paper/2502.15363/) (2025)

## Built on by (most cited first, 5 of 5)

- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)
- [Real-Time Multimodal Data Collection Using Smartwatches and Its Visualization in Education](https://mlsanity.com/paper/2512.02651/) (2025)
- [AISSA: Implementation and Deployment of an AI-based Student Slides Analysis tool for Academic Presentations](https://mlsanity.com/paper/2605.04729/) (2026)
- [AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education](https://mlsanity.com/paper/2605.04740/) (2026)

## Abstract (arXiv)

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 cognitive load, among others. Finally, students review their own performance through video recordings and engage in self-assessment and feedback visualization, comparing their own evaluations with peers and professors' assessments, class averages, and AI-generated recommendations. By combining human-based and data-based evaluation techniques, this framework enables more accurate, personalized and actionable feedback. We tested MOSAIC-F in the context of improving oral presentation skills.