collaborators

5 papers

cs.HC2025

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review

Alvaro Becerra, Ruth Cobos, Charles Lang

In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores th…

cs.CY2025

AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning

Alvaro Becerra, Roberto Daza, Ruth Cobos +3

This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-base…

cs.HC2025

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

Alvaro Becerra, Daniel Andres, Pablo Villegas +2

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), Obse…

cs.HC2025

M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards

Alvaro Becerra, Roberto Daza, Ruth Cobos +2

We present a demonstration of a web-based system called M2LADS ("System for Generating Multimodal Learning Analytics Dashboards"), designed to integrate, synchronize, visualize, an…

cs.HC2024

A multimodal dataset for understanding the impact of mobile phones on remote online virtual education

Roberto Daza, Alvaro Becerra, Ruth Cobos +2

This work presents the IMPROVE dataset, a multimodal resource designed to evaluate the effects of mobile phone usage on learners during online education. It includes behavioral, bi…