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Assia Boukezzi

dblp:403/8013 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0002-5274-2594ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 39% Immersive interaction · 30% Human-robot interaction · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
augmented feedback
0.912025
Mentor-Guided Learning in Immersive Virtual Environments: The Impact of Visual and Haptic Feedback on Skill Acquisition · IEEE Trans. Vis. Comput. Graph. 2025
Learning and educational technologies
skill acquisition
0.912025
Mentor-Guided Learning in Immersive Virtual Environments: The Impact of Visual and Haptic Feedback on Skill Acquisition · IEEE Trans. Vis. Comput. Graph. 2025
Immersive interaction
virtual reality
0.912025
Mentor-Guided Learning in Immersive Virtual Environments: The Impact of Visual and Haptic Feedback on Skill Acquisition · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

user study · 0.9NASA-TLX · 0.9
YearPublicationVenuePosition
2025 Mentor-Guided Learning in Immersive Virtual Environments: The Impact of Visual and Haptic Feedback on Skill Acquisition
abstract
In the early stages of learning a technical skill, trainees require guidance from a mentor through augmented feedback to develop higher expertise. However, the impact of such feedback and the different modalities used to communicate it remain underexplored in immersive virtual environments (IVE). This paper presents a study in which 27 participants were divided into three groups to learn a tool manipulation trajectory in an IVE. Two experimental groups received guidance from an expert using visual and/or haptic augmented feedback, while the control group received no feedback. The results indicate that both experimental groups showed significantly greater improvement in tool trajectory performance than the control group from pre- to post-test, with no significant differences between them. Analysis of their learning curves revealed similar performance improvements in tool trajectory across trials, outperforming the control group. Additionally, the visual-haptic feedback condition was linked to lower task load in three out of six dimensions of the NASA-TLX and a higher perceived interdependence with the expert's actions. These findings suggest that augmented feedback from an expert enhances the learning of tool manipulation skills. Although adding haptic feedback did not lead to better learning outcomes compared to visual feedback alone, it did enhance the overall user experience. These results offer valuable insights for designing IVEs that support mentor-trainee interactions through augmented feedback.
Flavien Lebrun, Cassandre Simon, Assia Boukezzi, Samir Otmane, Amine Chellali
IEEE Trans. Vis. Comput. Graph.3