Enhancing Self-Efficacy in VR Kendama Juggling Task Through Intervention and Adjustment of Virtual Success Experience

In systems where agents teach skills in VR or digital applications, the focus is often on enhancing skill acquisition efficiency. This study focused on self-efficacy, defined as the belief in one’s ability to succeed in a specific behavior. Despite its significance, discussions in prior research have been qualitative and relied on subjective participant reports. This study aimed to quantitatively investigate the impact of successful personal experiences, observation of others’ successes, and positive reinforcement on self-efficacy in motor skills tasks. It also explores factors such as difficulty adjusting, performance differences between observing others and oneself, and experience sequences, which are often overlooked in self-efficacy discussions. By leveraging VR, we implemented Kendama juggling, which facilitated the observation of others’ successes, provided difficulty levels, and measured quantitative performance. Experiments examined whether modifying conditions such as performance differences, experience sequence, difficulty adjustment, and the presence of rewards would improve self-efficacy. The results suggested that self-efficacy improves when others demonstrate slightly better skills when observing others’ experiences before their own and with rewards, while lowering difficulty levels does not contribute significantly. This study established a methodology for designing assistive systems to improve self-efficacy through quantitative analysis.

Authors

Institutions

Publication Details

Journal
Journal of Robotics and Mechatronics
Published
2025-06-19
DOI
https://doi.org/10.20965/jrm.2025.p0614
Primary Topic
Virtual Reality Applications and Impacts
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Self-Efficacy in VR Kendama Juggling Task Through Intervention and Adjustment of Virtual Success Experience

Tetsunari Inamura, Nanami Takahashi, K. Nagata
Journal of Robotics and Mechatronics
Virtual Reality Applications and Impacts
article

Enhancing Self-Efficacy in VR Kendama Juggling Task Through Intervention and Adjustment of Virtual Success Experience

Tetsunari Inamura, Nanami Takahashi, K. Nagata
article en

Abstract

In systems where agents teach skills in VR or digital applications, the focus is often on enhancing skill acquisition efficiency. This study focused on self-efficacy, defined as the belief in one’s ability to succeed in a specific behavior. Despite its significance, discussions in prior research have been qualitative and relied on subjective participant reports. This study aimed to quantitatively investigate the impact of successful personal experiences, observation of others’ successes, and positive reinforcement on self-efficacy in motor skills tasks. It also explores factors such as difficulty adjusting, performance differences between observing others and oneself, and experience sequences, which are often overlooked in self-efficacy discussions. By leveraging VR, we implemented Kendama juggling, which facilitated the observation of others’ successes, provided difficulty levels, and measured quantitative performance. Experiments examined whether modifying conditions such as performance differences, experience sequence, difficulty adjustment, and the presence of rewards would improve self-efficacy. The results suggested that self-efficacy improves when others demonstrate slightly better skills when observing others’ experiences before their own and with rewards, while lowering difficulty levels does not contribute significantly. This study established a methodology for designing assistive systems to improve self-efficacy through quantitative analysis.

Journal of Robotics and MechatronicsVol. 37(3)
Tamagawa University (JP), National Institute of Informatics (JP), Soka University (JP)
Japan Society for the Promotion of Science, Japan Science and Technology Agency
Openalex Percentile: Top 87%
Virtual Reality Applications and Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.