{"title":"体感技术能促进学生的学习吗?一个荟萃分析","authors":"Qingding Yu, Kun Yu","doi":"10.1177/07356331231176741","DOIUrl":null,"url":null,"abstract":"Body movements are regarded as part of the learning process. With the evolution of motion-sensing technology (MST) (e.g., Kinect, Xtion Pro, and Leap Motion), educational researchers try to explore the effect of MST on learning. However, the effect of MST on learning performance is still unclear. This is the first meta-analysis that aims to examine the effectiveness of MST on students’ learning. A total of 48 effect sizes from 37 independent and high-quality studies are analyzed, and the result suggests that MST has an upper-medium effect on learning (SMD = .574, 95% CI = [.450, .698], p < .001), particularly in the affective domain (SMD = .822). Moreover, three of eleven moderators (i.e., the subject, site of learning, and region) have moderating effects. The moderator analysis indicates that the following conditions are more conducive to MST-assisted learning: (1) 31∼50 students, (2) middle school, (3) >1 month, (4) No-STEM subjects, (5) game-based learning, (6) small group + individual learning, (7) high embodied level, (8) classroom, and (9) Asia and Europe. Finally, the discussions, implications, limitations, and future research directions are put forward.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":" ","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Does Motion-Sensing Technology Enhance Students’ Learning? A Meta-Analysis\",\"authors\":\"Qingding Yu, Kun Yu\",\"doi\":\"10.1177/07356331231176741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Body movements are regarded as part of the learning process. With the evolution of motion-sensing technology (MST) (e.g., Kinect, Xtion Pro, and Leap Motion), educational researchers try to explore the effect of MST on learning. However, the effect of MST on learning performance is still unclear. This is the first meta-analysis that aims to examine the effectiveness of MST on students’ learning. A total of 48 effect sizes from 37 independent and high-quality studies are analyzed, and the result suggests that MST has an upper-medium effect on learning (SMD = .574, 95% CI = [.450, .698], p < .001), particularly in the affective domain (SMD = .822). Moreover, three of eleven moderators (i.e., the subject, site of learning, and region) have moderating effects. The moderator analysis indicates that the following conditions are more conducive to MST-assisted learning: (1) 31∼50 students, (2) middle school, (3) >1 month, (4) No-STEM subjects, (5) game-based learning, (6) small group + individual learning, (7) high embodied level, (8) classroom, and (9) Asia and Europe. Finally, the discussions, implications, limitations, and future research directions are put forward.\",\"PeriodicalId\":47865,\"journal\":{\"name\":\"Journal of Educational Computing Research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational Computing Research\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1177/07356331231176741\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Computing Research","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/07356331231176741","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
摘要
身体动作被认为是学习过程的一部分。随着体感技术(如Kinect、Xtion Pro和Leap Motion)的发展,教育研究者试图探索体感技术对学习的影响。然而,MST对学习绩效的影响尚不清楚。这是第一个旨在检验MST对学生学习有效性的元分析。我们对来自37个独立高质量研究的48个效应量进行了分析,结果表明MST对学习具有中上效应(SMD = .574, 95% CI =[])。450, 0.698], p < 0.001),尤其是在情感领域(SMD = 0.822)。此外,11个调节因子中有3个(即,主题、学习地点和地区)具有调节作用。调节因子分析表明,以下条件更有利于mst辅助学习:(1)31 ~ 50名学生,(2)中学,(3)>1个月,(4)无stem科目,(5)基于游戏的学习,(6)小组+个人学习,(7)高体现水平,(8)课堂,(9)亚洲和欧洲。最后,提出了本文的讨论、启示、局限性和未来的研究方向。
Does Motion-Sensing Technology Enhance Students’ Learning? A Meta-Analysis
Body movements are regarded as part of the learning process. With the evolution of motion-sensing technology (MST) (e.g., Kinect, Xtion Pro, and Leap Motion), educational researchers try to explore the effect of MST on learning. However, the effect of MST on learning performance is still unclear. This is the first meta-analysis that aims to examine the effectiveness of MST on students’ learning. A total of 48 effect sizes from 37 independent and high-quality studies are analyzed, and the result suggests that MST has an upper-medium effect on learning (SMD = .574, 95% CI = [.450, .698], p < .001), particularly in the affective domain (SMD = .822). Moreover, three of eleven moderators (i.e., the subject, site of learning, and region) have moderating effects. The moderator analysis indicates that the following conditions are more conducive to MST-assisted learning: (1) 31∼50 students, (2) middle school, (3) >1 month, (4) No-STEM subjects, (5) game-based learning, (6) small group + individual learning, (7) high embodied level, (8) classroom, and (9) Asia and Europe. Finally, the discussions, implications, limitations, and future research directions are put forward.
期刊介绍:
The goal of this Journal is to provide an international scholarly publication forum for peer-reviewed interdisciplinary research into the applications, effects, and implications of computer-based education. The Journal features articles useful for practitioners and theorists alike. The terms "education" and "computing" are viewed broadly. “Education” refers to the use of computer-based technologies at all levels of the formal education system, business and industry, home-schooling, lifelong learning, and unintentional learning environments. “Computing” refers to all forms of computer applications and innovations - both hardware and software. For example, this could range from mobile and ubiquitous computing to immersive 3D simulations and games to computing-enhanced virtual learning environments.