# M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education

**Authors:** Álvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Mutlu Cukurova, Julian Fierrez  
**arXiv:** [2305.12561](https://arxiv.org/abs/2305.12561)  
**Date:** 2023-05-21  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2305.12561v1/

## Key points

- M2LADS is a web-based system that integrates and visualizes multimodal data recorded during learning sessions in MOOCs.
- The system gathers biometric and behavioral signals, including electroencephalogram data, heart rate, and visual attention from video recordings.
- Learners' static background data and their learning performance measures are also tracked using LOGCE and MOOC tracking logs respectively.
- The M2LADS system generates web-based dashboards that visualize this multimodal data, allowing instructors to monitor students' progress and provide personalized feedback.
- By analyzing this data, instructors can identify areas where students struggle or excel and adjust course content accordingly.
- M2LADS provides opportunities to enhance learning analytics models by integrating multiple sources of data into one dashboard.
- This approach has the potential to improve learning outcomes through feedback visualizations and interventions while also enhancing open content in MOOCs.

## Summary

The article "M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education" presents a web-based system that supports the integration and visualization of multimodal data recorded during learning sessions in Massive Open Online Courses (MOOCs). The M2LADS system is based on the edBB platform and gathers biometric and behavioral signals, including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures, and visual attention from video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGCE and MOOC tracking logs respectively. All this information is included in the Web-based System, providing opportunities to capture learners' holistic experience during their interactions with the MOOC. The M2LADS system generates web-based dashboards that visualize this multimodal data, allowing instructors to monitor students' progress and provide personalized feedback. By analyzing this data, instructors can identify areas where students struggle or excel and adjust course content accordingly. Moreover, M2LADS provides opportunities to enhance learning analytics models by integrating multiple sources of data into one dashboard. Overall, the M2LADS system offers a comprehensive approach to capturing learners' experiences during MOOC interactions. This approach has the potential to improve learning outcomes through feedback visualizations and interventions while also enhancing open content in MOOCs. The authors hope that their work will inspire further research into how multimodal data can be used to improve online education delivery systems.