多媒体学习原理在规模上预测测验表现

Anita B. Delahay, M. Lovett
{"title":"多媒体学习原理在规模上预测测验表现","authors":"Anita B. Delahay, M. Lovett","doi":"10.1145/3231644.3231694","DOIUrl":null,"url":null,"abstract":"Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multimedia learning principles at scale predict quiz performance\",\"authors\":\"Anita B. Delahay, M. Lovett\",\"doi\":\"10.1145/3231644.3231694\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.\",\"PeriodicalId\":20634,\"journal\":{\"name\":\"Proceedings of the Fifth Annual ACM Conference on Learning at Scale\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth Annual ACM Conference on Learning at Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3231644.3231694\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3231644.3231694","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

经验支持的多媒体学习(MML)原则[1]提出了设计教学的有效方法,通常是针对图形或活动的顺序元素。我们研究了MML的积极影响是否可以在MOOC的大型教学单元中检测到。我们编码了与MML原则相对应的教学设计(ID)特征,将测试项目映射到这些特征以及MOOC参与者对它们的使用,并试图预测测试表现。我们发现与MML相关的教学特征,即具有高质量示例和简明文本的实践问题,具有积极的预测性。我们认为,从教学材料的特征来预测测验项目的表现是可能的,并建议将这种方法扩展到ID的其他方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimedia learning principles at scale predict quiz performance
Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.
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
自引率
0.00%
发文量
0
×
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