Jhon Bueno Vesga

dblp:265/2908 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
0000-0002-7903-5921ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 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
2 papers
Usability and user experience research · 45% Immersive interaction · 26% Learning and educational technologies · 18%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usability and user experience research
cognitive load
1.012026
The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System · IEEE Trans. Vis. Comput. Graph. 2026
Usability and user experience research
technology acceptance
1.012026
The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System · IEEE Trans. Vis. Comput. Graph. 2026
Immersive interaction
virtual reality training
1.012026
The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System · IEEE Trans. Vis. Comput. Graph. 2026
Health and well-being technologies
cognitive engagement
0.512021
The Effects of Cognitive Load on Engagement in a Virtual Reality Learning Environment · VR 2021
Learning and educational technologies › immersive learning
virtual reality learning environment
0.512021
The Effects of Cognitive Load on Engagement in a Virtual Reality Learning Environment · VR 2021
Learning and educational technologies
nursing education
0.312026
The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System · IEEE Trans. Vis. Comput. Graph. 2026
Immersive interaction › virtual reality
presence
0.112021
The Effects of Cognitive Load on Engagement in a Virtual Reality Learning Environment · VR 2021

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

technology acceptance model · 1.0semi-structured interviews · 1.0mixed-methods study · 1.0hierarchical regression analysis · 0.5
YearPublicationVenuePosition
2026 The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System
abstract
The Technology Acceptance Model (TAM) has been used extensively to understand technology adoption in the context of virtual reality (VR). The model includes external variables that are important drivers of attitudes towards adopting technology. In this mixed-methods study, we assessed the effects of cognitive engagement and individual dimensions of cognitive load (CL) on the attitudes driving the intention to use an AI-supported VR system for nursing students' patient management training: perceived usefulness (PU) and perceived ease of use (PEOU). The participants were a group of nursing students from a university in the Midwestern United States. We also explored interview data to understand the participants' perceptions about the resulting factors. The quantitative results showed that engagement and PEOU are significant predictors of PU, and so are frustration (one of the dimensions of CL) and engagement with PEOU. Interview data revealed that participants' frustrations did not always have a negative effect. They generally enhanced their engagement by making the scenarios feel realistic and valuable for skill development.
Jhon Bueno Vesga, Hao He 0009, Shangman Li, Yupei Duan
IEEE Trans. Vis. Comput. Graph.1
2023 Outdated or Not? A Case Study of How 3D Desktop VR Is Accepted Today
Hao He 0009, Jhon Bueno Vesga, Shangman Li
iLRN3
2021 The Effects of Cognitive Load on Engagement in a Virtual Reality Learning Environment
abstract
Engagement has been traditionally linked to presence in desktop-based virtual reality learning environments. Although several studies have been performed to determine other factors affecting cognitive engagement, the role of cognitive load as a factor of student's engagement in desktop-based virtual reality (VR) has received little attention in the literature. The main purpose of this study was to explain if individual dimensions of cognitive load (mental demand, effort, and frustration level) can be used in addition to factors like presence and self-efficacy to predict student's cognitive engagement. The results of the study confirmed presence and self-efficacy as significant predictors of student's engagement. Also, a three-step hierarchical regression analysis revealed that two of the three individual dimensions of cognitive load (effort and frustration level) were also significant predictors of student's engagement.
Jhon Bueno Vesga, Hao He 0009
VR1