How effective is immersive VR for vocational education? Analyzing knowledge gains and motivational effects

职业教育 计算机科学 虚拟现实 人机交互 心理学 多媒体 教育学
作者
Herbert Thomann,Jan Zimmermann,Viola Deutscher
出处
期刊:Computers & education [Elsevier BV]
卷期号:220: 105127-105127 被引量:6
标识
DOI:10.1016/j.compedu.2024.105127
摘要

While Immersive Virtual Reality (IVR) technology has been predominantly employed in technical and medical academic education, it also holds significant potential for Vocational Education and Training (VET). IVR's unique properties, such as high immersion could be especially beneficial in VET, where action-oriented skills, domain-specific knowledge, and their application in new work contexts are crucial. This study investigates the effectiveness of IVR in vocational education, focusing on (1) objective knowledge acquisition, (2) subjectively perceived knowledge acquisition, and (3) motivational effects in the domain of warehouse logistics. Through a randomized controlled trial with 72 vocational students, we compared IVR-based learning to traditional paper-based methods. Results show that IVR did not improve immediate declarative knowledge acquisition; in fact, the paper-pencil group outperformed the IVR group on an objective post-test. However, IVR significantly enhanced students' perceived knowledge gains. The study also confirms higher motivation and immersion in IVR settings compared to paper-based learning environments. The identified discrepancy between perceived and actual learning may help explain the unclear state of research regarding knowledge acquisition in IVR studies, based on the measures used. Moreover, the findings underscore the necessity for a nuanced approach to IVR implementation in VET education. While IVR can be recommended for enhancing short-term learner engagement, traditional methods or a blend of IVR and non-immersive techniques may be more effective for fostering declarative knowledge in the short term.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
蜚英腾茂完成签到,获得积分10
刚刚
IFYK完成签到,获得积分10
1秒前
科研白发布了新的文献求助10
2秒前
demon1完成签到,获得积分10
2秒前
板栗发布了新的文献求助10
3秒前
肥大鸭完成签到,获得积分10
4秒前
EinZwei完成签到,获得积分10
5秒前
6秒前
6秒前
蜚英腾茂发布了新的文献求助10
6秒前
魁拔蛮吉发布了新的文献求助10
7秒前
哒哒哒发布了新的文献求助10
7秒前
8秒前
俄而完成签到 ,获得积分10
8秒前
9秒前
英姑应助阔达苡采纳,获得10
10秒前
10秒前
sun完成签到,获得积分10
10秒前
小民完成签到 ,获得积分10
12秒前
所所应助Bonnienuit采纳,获得10
12秒前
12秒前
MMCC应助陈好采纳,获得50
13秒前
zzz发布了新的文献求助30
14秒前
华仔应助健壮书包采纳,获得20
14秒前
15秒前
XXW发布了新的文献求助10
15秒前
明理代真发布了新的文献求助10
15秒前
15秒前
18秒前
小李飞刀完成签到,获得积分10
19秒前
19秒前
wwe发布了新的文献求助10
20秒前
打打应助科研白采纳,获得10
20秒前
今后应助ATOM采纳,获得10
20秒前
21秒前
21秒前
可靠寒云发布了新的文献求助10
21秒前
大航海家完成签到,获得积分10
21秒前
21秒前
22秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7256133
求助须知:如何正确求助?哪些是违规求助? 8878255
关于积分的说明 18750802
捐赠科研通 6936413
什么是DOI,文献DOI怎么找? 3200785
关于科研通互助平台的介绍 2374970
邀请新用户注册赠送积分活动 2176314