mlsanityData to 7 Oct 2026

VAAD: Visual Attention Analysis Dashboard applied to e-Learning

arXiv 2405.20091 ↗First posted 30 May 2024cs.CV cs.HC cs.LGcited by 12

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Abstract (arXiv)

In this paper, we present an approach in the Multimodal Learning Analytics field. Within this approach, we have developed a tool to visualize and analyze eye movement data collected during learning sessions in online courses. The tool is named VAAD, an acronym for Visual Attention Analysis Dashboard. These eye movement data have been gathered using an eye-tracker and subsequently processed and visualized for interpretation. The purpose of the tool is to conduct a descriptive analysis of the data by facilitating its visualization, enabling the identification of differences and learning patterns among various learner populations. Additionally, it integrates a predictive module capable of anticipating learner activities during a learning session. Consequently, VAAD holds the potential to offer valuable insights into online learning behaviors from both descriptive and predictive perspectives.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

8 of 33 references.

References · 8

  1. 2020mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimationcited by 19
  2. 2022edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platformscited by 18
  3. 2019edBB: Biometrics and Behavior for Assessing Remote Educationcited by 17
  4. 2023M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Educationcited by 15
  5. 2021ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learningcited by 13
  6. 2020Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBBcited by 12
  7. 2023mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detectioncited by 9
  8. 2023MATT: Multimodal Attention Level Estimation for e-learning Platformscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 3.

12 papers.