{"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":null,"pages":null},"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\":null,\"pages\":null},\"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}
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.