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Existing research predominantly focuses on the performance of VR tutorial modes, such as the placement of text and diagrams within tutorial content. However, few studies have delved into other attributes of tutorials. This study categorizes 4 VR game tutorial modes based on time flow: (1) traditional instruction screen, (2) slow motion, (3) bullet time, and (4) context-sensitive mode. This paper evaluates the impact of these 4 VR game tutorial modes with varying time flow rates on controls learnability, engagement-related outcomes, and player performance. We conducted a between-subjects experiment with 59 participants. Results indicated that bullet time significantly enhanced controls learnability, reduced cognitive load, and improved player performance when compared to other tutorial modes. Our research contributes to a more comprehensive understanding of VR game tutorials and offers practical guidance for game designers, underscoring the potential of bullet time to enhance learnability and game experience.<\/jats:p>","DOI":"10.1145\/3651296","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T13:39:44Z","timestamp":1715607584000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Impact of Tutorial Modes with Different Time Flow Rates in Virtual Reality Games"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9645-4526","authenticated-orcid":false,"given":"Boyuan","family":"Chen","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7469-3980","authenticated-orcid":false,"given":"Xinan","family":"Yan","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1305-2081","authenticated-orcid":false,"given":"Xuning","family":"Hu","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7732-6258","authenticated-orcid":false,"given":"Dominic","family":"Kao","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, Indiana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3600-8955","authenticated-orcid":false,"given":"Hai-Ning","family":"Liang","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Forecast augmented (AR) and virtual reality (VR) market size worldwide from 2020 to","author":"Alsop Thomas","year":"2024","unstructured":"Thomas Alsop. 2021. 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