通过当前学习表现和学习行为指标检测早期学生的风险

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Cybernetics and Information Technologies Pub Date : 2022-03-01 DOI:10.2478/cait-2022-0008
T. A. Kustitskaya, A. A. Kytmanov, M. Noskov
{"title":"通过当前学习表现和学习行为指标检测早期学生的风险","authors":"T. A. Kustitskaya, A. A. Kytmanov, M. Noskov","doi":"10.2478/cait-2022-0008","DOIUrl":null,"url":null,"abstract":"Abstract The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requirements to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term.","PeriodicalId":45562,"journal":{"name":"Cybernetics and Information Technologies","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators\",\"authors\":\"T. A. Kustitskaya, A. A. Kytmanov, M. Noskov\",\"doi\":\"10.2478/cait-2022-0008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requirements to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term.\",\"PeriodicalId\":45562,\"journal\":{\"name\":\"Cybernetics and Information Technologies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cybernetics and Information Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/cait-2022-0008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cybernetics and Information Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cait-2022-0008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 6

摘要

摘要本文着重探讨了早期预测学生学习失败的问题,目的是通过及时引入支持措施来预防学生学习失败。我们提出了一种为以混合学习形式教授的学术课程或模块设计预测模型的方法。我们介绍了预测模型在教育过程中的适用性的某些要求,如可解释性、可操作性和对课程设计的适应性。我们测试了满足这些要求的三种类型的分类器,并从学期的早期阶段开始选择一种表现最好的分类器,从而为及时支持有风险的学生提供各种机会。我们的实证研究证实,所提出的方法对高等教育机构早期预警系统的开发是有希望的。这样的系统可以积极影响学生的保留率,并长期提高学习和教学体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators
Abstract The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requirements to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
期刊最新文献
A Review on State-of-Art Blockchain Schemes for Electronic Health Records Management Degradation Recoloring Deutan CVD Image from Block SVD Watermark Integration Approaches for Heterogeneous Big Data: A Survey Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition Hybrid Edge Detection Methods in Image Steganography for High Embedding Capacity
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1