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

arXiv: https://arxiv.org/abs/2305.12561 · HTML: https://mlsanity.com/paper/2305.12561/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 10 of 10)

- [Detecting Attended Visual Targets in Video](https://mlsanity.com/paper/2003.02501/) (2020)
- [Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment](https://mlsanity.com/paper/2302.10908/) (2023)
- [mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation](https://mlsanity.com/paper/2006.05327/) (2020)
- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [edBB: Biometrics and Behavior for Assessing Remote Education](https://mlsanity.com/paper/1912.04786/) (2019)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning](https://mlsanity.com/paper/2112.09165/) (2021)
- [Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB](https://mlsanity.com/paper/2006.00825/) (2020)
- [MATT: Multimodal Attention Level Estimation for e-learning Platforms](https://mlsanity.com/paper/2301.09174/) (2023)
- [An Artificial Intelligence driven Learning Analytics Method to Examine the Collaborative Problem solving Process from a Complex Adaptive Systems Perspective](https://mlsanity.com/paper/2210.16059/) (2022)

## Built on by (most cited first, 15 of 15)

- [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)
- [Biometrics and Behavior Analysis for Detecting Distractions in e-Learning](https://mlsanity.com/paper/2405.15434/) (2024)
- [mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection](https://mlsanity.com/paper/2309.07880/) (2023)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)
- [SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality](https://mlsanity.com/paper/2501.10977/) (2025)
- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback](https://mlsanity.com/paper/2506.08634/) (2025)
- [M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards](https://mlsanity.com/paper/2502.15363/) (2025)
- [Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review](https://mlsanity.com/paper/2509.07742/) (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)
- [Dual-Stream Cross-Modal Representation Learning via Residual Semantic Decorrelation](https://mlsanity.com/paper/2512.07568/) (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 Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures, visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGCE and MOOC tracking logs respectively, and both are included in the Web-based System. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the MOOC, which can in turn be used to improve their learning outcomes through feedback visualizations and interventions, as well as to enhance learning analytics models and improve the open content of the MOOC.