AF-Mix
A gaze-aware learning system with attention feedback in mixed reality
Bibliographic Data
| ID | 21642645 |
|---|---|
| Authors | Shi Liu (0000-0003-3381-7108, Karlsruhe Institute of Technology, corresponding author), Peyman Toreini (0000-0002-2468-1715, Karlsruhe Institute of Technology), Alexander Maedche (0000-0001-6546-4816, Karlsruhe Institute of Technology) |
| Year | 2025 |
| Volume | 198 |
| Pages | 103467 |
| Publication date | 2025-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2025.103467 |
| OpenAlex | W4407761766 |
| Language | EN |
| References cited | 31 |
Mixed Reality (MR) has demonstrated its potential in various learning contexts. MR-based learning environments empower users to actively explore learning content visualized in multiple formats, such as 3D models, videos, and images. Nonetheless, the sophisticated visualizations in MR learning environments may result in potential visual overload, posing a challenge for users in efficiently allocating their attention. In this paper, we present AF-Mix, a learning support system that leverages eye tracking sensors in Microsoft HoloLens 2 to offer attention feedback for learners. Aiming to design AF-Mix, we conducted a participatory design study and integrated the attention feedback into our system, following users’ needs and suggestions. Furthermore, we evaluated AF-Mix in an evaluation study (n = 22) following a quantitative analysis of users’ visual behavior, as well as a qualitative analysis of interview transcripts. Our findings show that providing feedback to support the learning process can be achieved effectively with eye tracking. In specific, attention feedback assists learners in retrieving previously missed information and encourages learners to reallocate their attention in the review process. Moreover, providing personalized feedback based on previous attention allocation is more effective in supporting users than a self-review approach without gaze-aware assistance in MR. Such feedback facilitates users in managing their limited attentional resources better and supports the reflection of their learning journey more effectively. • Human-centered design of attention feedback in Mixed Reality (MR) learning systems. • Evaluation of the system using qualitative and quantitative methods. • Attention feedback in MR supports users in attention management and self-reflection. • Design recommendations for supporting self-reflection in MR learning systems
Augmented reality · Cognitive psychology · Gaze · Mixed reality · Augmented Reality Applications · Computer Science · Gaze Tracking and Assistive Technology · Psychology · Virtual Reality Applications and Impacts · Artificial Intelligence
Research trends in the use of augmented reality in science education
Virtual, augmented and mixed reality in K–12 education
A review of using eye-tracking technology in exploring learning from 2000 to 2012
Advantages and challenges associated with augmented reality for education
Animating eco-education
Should There Be a Three-Strikes Rule Against Pure Discovery Learning?
Participatory design
Bayesian t tests for accepting and rejecting the null hypothesis
Feedback and Self-Regulated Learning
A Scaffolding Design Framework for Software to Support Science Inquiry
Why Minimal Guidance During Instruction Does Not Work
A Review of Self-regulated Learning
The Jasp guidelines for conducting and reporting a Bayesian analysis
HoloLens and mobile augmented reality in medical and health science education
The group elicitation method for participatory design and usability testing
One size fits all? What counts as quality practice in (reflexive) thematic analysis
Using thematic analysis in psychology
| Citation velocity | historical |
|---|---|
| Highly cited | No |