VAAD: Visual Attention Analysis Dashboard applied to e-learning

Abstract

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn 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 perspectivesSupport by projects: HumanCAIC (TED2021-131787B-I00 MICINN), SNOLA (RED2022-134284-T), e-Madrid-CM (S2018/TCS-4307, a project which is co-funded by the European Structural Funds, FSE and FEDER), IndiGo! (PID2019-105951RB-I00), TEA360 (PID2023-150488OB-I00, SPID202300X150488IV0), BIO-PROCTORING (GNOSS Program, Agreement Ministerio de Defensa-UAM-FUAM dated 29-03-2022), and Cátedra ENIA UAM-VERIDAS en IA Responsable (NextGenerationEU PRTR TSI-100927-2023-2). Roberto Daza is supported by a FPI fellowship from MINECO/FEDE