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VAAD: Visual Attention Analysis Dashboard applied to e-Learning

Miriam Navarro, Álvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Julian Fierrez

TL;DR

A tool to visualize and analyze eye movement data collected during learning sessions in online courses, named VAAD, which integrates a predictive module capable of anticipating learner activities during a learning session.

Abstract

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.

VAAD: Visual Attention Analysis Dashboard applied to e-Learning

TL;DR

A tool to visualize and analyze eye movement data collected during learning sessions in online courses, named VAAD, which integrates a predictive module capable of anticipating learner activities during a learning session.

Abstract

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.
Paper Structure (10 sections, 3 figures, 5 tables)

This paper contains 10 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works and motivation for the approach proposed
  3. Context and Dataset
  4. Eye-tracking Data
  5. M2LADS Data
  6. Approach proposed
  7. Processing Data Module
  8. Visualization Module
  9. Prediction Module
  10. Conclusions and Future work

Figures (3)

  • Figure 1: VAAD Architecture/Modules
  • Figure 2: Screenshot from an example overview of global analysis for reading actuvity (a) and video watching (b)
  • Figure 3: Screenshot from an example overview of individual analysis (a) and screenshot from the video that the learner is watching (b)