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

• URL: http://arxiv.org/abs/2506.08634v1
• Date: Tue, 10 Jun 2025 09:46:31 GMT
• Title: MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
• Authors: Alvaro Becerra, Daniel Andres, Pablo Villegas, Roberto Daza, Ruth Cobos,
• Abstract summary: This framework integrates Multimodal Learning Analytics (MMLA), Observations, Sensors, Artificial Intelligence (AI), and Collaborative assessments.<n>By combining human-based and data-based evaluation techniques, this framework enables more accurate, personalized and actionable feedback.
• Score: 1.0835264351334324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education [3.557803321422781]
  This article presents a scalable, AI-supported framework for qualitative student feedback using large language models.<n>The system employs hierarchical summarization, anonymization, and exception handling to extract actionable themes from open-ended comments.<n>We report on its successful deployment across a large college of engineering.
  arXiv  Detail & Related papers  (2025-08-01T20:27:40Z)
• Leadership Assessment in Pediatric Intensive Care Unit Team Training [12.775569777482566]
  This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision.<n>We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment.
  arXiv  Detail & Related papers  (2025-05-30T09:19:33Z)
• Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback [59.768119380109084]
  This paper introduces an interactive continual learning paradigm where AI models dynamically learn new skills from real-time human feedback.<n>We propose RiCL, a Reinforced interactive Continual Learning framework leveraging Large Language Models (LLMs)<n>Our RiCL approach substantially outperforms existing combinations of state-of-the-art online continual learning and noisy-label learning methods.
  arXiv  Detail & Related papers  (2025-05-15T03:22:03Z)
• Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach [7.273857543125784]
  Our study proposes a novel text-centered multimodal fusion architecture to assess the quality of three discourse components grounded in the Global Teaching InSights (GTI) observation protocol.<n>We employ attention mechanisms to capture inter- and intra-modal interactions from transcript, audio, and video streams.<n>Our results highlight the dominant role of text modality in approaching this task.
  arXiv  Detail & Related papers  (2025-05-12T09:24:21Z)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey [64.08485471150486]
  This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings.<n>We systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication.
  arXiv  Detail & Related papers  (2025-03-28T14:08:40Z)
• Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT [7.273857543125784]
  Our work explores a multimodal approach to automatically estimating encouragement and warmth in classrooms. We employed facial and speech emotion recognition with sentiment analysis to extract interpretable features from video, audio, and transcript data. We demonstrated our approach on the GTI dataset, comprising 367 16-minute video segments from 92 authentic lesson recordings.
  arXiv  Detail & Related papers  (2024-04-01T16:58:09Z)
• Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels [95.44077384918725]
  We propose to teach large multi-modality models (LMMs) with text-defined rating levels instead of scores. The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA) and video quality assessment (VQA) tasks.
  arXiv  Detail & Related papers  (2023-12-28T16:10:25Z)
• Constructive Large Language Models Alignment with Diverse Feedback [76.9578950893839]
  We introduce Constructive and Diverse Feedback (CDF) as a novel method to enhance large language models alignment. We exploit critique feedback for easy problems, refinement feedback for medium problems, and preference feedback for hard problems. By training our model with this diversified feedback, we achieve enhanced alignment performance while using less training data.
  arXiv  Detail & Related papers  (2023-10-10T09:20:14Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features [31.59528815233441]
  We propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.<n> Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0.026 (0.990 vs 0.964 in seen noise environments) and 0.012 (0.969 vs 0.957 in unseen noise environments) in perceptual evaluation of speech quality (PESQ) prediction.
  arXiv  Detail & Related papers  (2021-11-03T17:30:43Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)
• Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems [3.269851859258154]
  A common way of circumventing label-scarce problems is pre-training a model to learn representations of the contents of learning items. We propose Assessment Modeling, a class of fundamental pre-training tasks for general interactive educational systems.
  arXiv  Detail & Related papers  (2020-01-01T02:00:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.