{"title":"自动写作评估对写作质量的有效性:一项元分析","authors":"Nasha Zhai, Xiaomei Ma","doi":"10.1177/07356331221127300","DOIUrl":null,"url":null,"abstract":"Automated writing evaluation (AWE) has been frequently used to provide feedback on student writing. Many empirical studies have examined the effectiveness of AWE on writing quality, but the results were inconclusive. Thus, the magnitude of AWE’s overall effect and factors influencing its effectiveness across studies remained unclear. This study re-examined the issue by meta-analyzing the results of 26 primary studies with a total of 2468 participants from 2010 to 2022. The results revealed that AWE had a large positive overall effect on writing quality (g = 0.861, p < 0.001). Further moderator analyses indicated that AWE was more effective for post-secondary students than for secondary students and had more benefits for English as a Foreign Language (EFL) and English as a Second Language (ESL) learners than for Native English Speaker (NES) learners. When the genre of writing was considered, AWE showed a more significant impact on argumentative writing than on academic and mixed writing genres. However, intervention duration, feedback combination, and AWE platform did not moderate the effect of AWE on writing quality. The implications and recommendations for both research and practice are discussed in depth.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"The Effectiveness of Automated Writing Evaluation on Writing Quality: A Meta-Analysis\",\"authors\":\"Nasha Zhai, Xiaomei Ma\",\"doi\":\"10.1177/07356331221127300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated writing evaluation (AWE) has been frequently used to provide feedback on student writing. Many empirical studies have examined the effectiveness of AWE on writing quality, but the results were inconclusive. Thus, the magnitude of AWE’s overall effect and factors influencing its effectiveness across studies remained unclear. This study re-examined the issue by meta-analyzing the results of 26 primary studies with a total of 2468 participants from 2010 to 2022. The results revealed that AWE had a large positive overall effect on writing quality (g = 0.861, p < 0.001). Further moderator analyses indicated that AWE was more effective for post-secondary students than for secondary students and had more benefits for English as a Foreign Language (EFL) and English as a Second Language (ESL) learners than for Native English Speaker (NES) learners. When the genre of writing was considered, AWE showed a more significant impact on argumentative writing than on academic and mixed writing genres. However, intervention duration, feedback combination, and AWE platform did not moderate the effect of AWE on writing quality. The implications and recommendations for both research and practice are discussed in depth.\",\"PeriodicalId\":47865,\"journal\":{\"name\":\"Journal of Educational Computing Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2022-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational Computing Research\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1177/07356331221127300\",\"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/10.1177/07356331221127300","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}
The Effectiveness of Automated Writing Evaluation on Writing Quality: A Meta-Analysis
Automated writing evaluation (AWE) has been frequently used to provide feedback on student writing. Many empirical studies have examined the effectiveness of AWE on writing quality, but the results were inconclusive. Thus, the magnitude of AWE’s overall effect and factors influencing its effectiveness across studies remained unclear. This study re-examined the issue by meta-analyzing the results of 26 primary studies with a total of 2468 participants from 2010 to 2022. The results revealed that AWE had a large positive overall effect on writing quality (g = 0.861, p < 0.001). Further moderator analyses indicated that AWE was more effective for post-secondary students than for secondary students and had more benefits for English as a Foreign Language (EFL) and English as a Second Language (ESL) learners than for Native English Speaker (NES) learners. When the genre of writing was considered, AWE showed a more significant impact on argumentative writing than on academic and mixed writing genres. However, intervention duration, feedback combination, and AWE platform did not moderate the effect of AWE on writing quality. The implications and recommendations for both research and practice are discussed in depth.
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.