Influence of Learning Intervention on Online Learners’ Performance

Q1 Social Sciences
Limin Ren, Kelu Fu, Ruifeng Cao
{"title":"Influence of Learning Intervention on Online Learners’ Performance","authors":"Limin Ren, Kelu Fu, Ruifeng Cao","doi":"10.3991/ijet.v18i16.42297","DOIUrl":null,"url":null,"abstract":"With the expansion of the online learning scale and the rapid development of learning analysis techniques, learning systems can completely record learners’ online learning behavior. What types of learning intervention measures can influence learning performance? How is the intervention working? The above questions are the focus of online courses and emphasize improving the online learning effect. In this research, 96 undergraduates (Classes 1 and 2, Grade 2) majoring in civil engineering and enrolled in the Engineering Surveying online course at the College of Transportation Engineering, Huanghe Jiaotong University, Henan Province, were selected as the research objects and randomly divided into two groups (the experimental group (N = 48) and the control group (N = 48)). Then, conducted a 15-week sustained intervention study through quasi-experimental research, and verified the effectiveness of different types of intervention measures on the learners’ learning performance. Next, a one-way analysis of variance was conducted, based on the pretesting of the performance data, the quality of online discussion posts, the effective learning duration, and the online final test results. Research results showed no obvious differences between the experimental group and control group in the online final test results from the previous semester (P = 0.347). The online learners’ learning performance was influenced by three aspects of the learning intervention, that is, apparent differences in the posting quality in the learning prompt intervention (T = 8.23, P < 0.01), the online learning duration in the process monitoring intervention (T = 23.19, P < 0.01), and the online final test results in the achievement incentive intervention (T = 6.08, P < 0.01). The research results have an important reference value for implementing intervention strategies in the online learning environment, judging the effectiveness of such strategies according to curve changes in performance, and using the mass data of learners recorded by online learning platforms.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Technologies in Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3991/ijet.v18i16.42297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

With the expansion of the online learning scale and the rapid development of learning analysis techniques, learning systems can completely record learners’ online learning behavior. What types of learning intervention measures can influence learning performance? How is the intervention working? The above questions are the focus of online courses and emphasize improving the online learning effect. In this research, 96 undergraduates (Classes 1 and 2, Grade 2) majoring in civil engineering and enrolled in the Engineering Surveying online course at the College of Transportation Engineering, Huanghe Jiaotong University, Henan Province, were selected as the research objects and randomly divided into two groups (the experimental group (N = 48) and the control group (N = 48)). Then, conducted a 15-week sustained intervention study through quasi-experimental research, and verified the effectiveness of different types of intervention measures on the learners’ learning performance. Next, a one-way analysis of variance was conducted, based on the pretesting of the performance data, the quality of online discussion posts, the effective learning duration, and the online final test results. Research results showed no obvious differences between the experimental group and control group in the online final test results from the previous semester (P = 0.347). The online learners’ learning performance was influenced by three aspects of the learning intervention, that is, apparent differences in the posting quality in the learning prompt intervention (T = 8.23, P < 0.01), the online learning duration in the process monitoring intervention (T = 23.19, P < 0.01), and the online final test results in the achievement incentive intervention (T = 6.08, P < 0.01). The research results have an important reference value for implementing intervention strategies in the online learning environment, judging the effectiveness of such strategies according to curve changes in performance, and using the mass data of learners recorded by online learning platforms.
学习干预对在线学习者学习成绩的影响
随着在线学习规模的扩大和学习分析技术的快速发展,学习系统可以完整地记录学习者的在线学习行为。哪些类型的学习干预措施会影响学习表现?干预的效果如何?以上问题是网络课程的重点,强调提高在线学习效果。本研究选取河南省黄河交通大学交通工程学院工程测量在线课程土木工程专业本科生96名(二年级一、二班)作为研究对象,随机分为实验组(N = 48)和对照组(N = 48)两组。然后,通过准实验研究进行了为期15周的持续干预研究,验证了不同类型干预措施对学习者学习绩效的有效性。接下来,基于绩效数据的预检验、在线讨论帖的质量、有效学习时长和在线最终测试结果,进行单向方差分析。研究结果显示,实验组与对照组在线期末考试成绩与上学期相比无明显差异(P = 0.347)。在线学习者的学习绩效受到学习干预三个方面的影响,即学习提示干预中发帖质量的显著差异(T = 8.23, P < 0.01),过程监控干预中在线学习时长的显著差异(T = 23.19, P < 0.01),成就激励干预中在线期末考试成绩的显著差异(T = 6.08, P < 0.01)。研究结果对于在线学习环境中实施干预策略,根据表现曲线变化判断干预策略的有效性,以及利用在线学习平台记录的大量学习者数据具有重要的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
352
审稿时长
12 weeks
期刊介绍: This interdisciplinary journal focuses on the exchange of relevant trends and research results and presents practical experiences gained while developing and testing elements of technology enhanced learning. It bridges the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Fields of interest include, but are not limited to: -Software / Distributed Systems -Knowledge Management -Semantic Web -MashUp Technologies -Platforms and Content Authoring -New Learning Models and Applications -Pedagogical and Psychological Issues -Trust / Security -Internet Applications -Networked Tools -Mobile / wireless -Electronics -Visualisation -Bio- / Neuroinformatics -Language /Speech -Collaboration Tools / Collaborative Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信