{"title":"功能工厂:众包功能发现","authors":"K. Veeramachaneni, Kiarash Adl, Una-May O’Reilly","doi":"10.1145/2724660.2728696","DOIUrl":null,"url":null,"abstract":"We examine the process of engineering features for developing models that improve our understanding of learners' online behavior in MOOCs. Because feature engineering relies so heavily on human insight, we engage the crowd for feature proposals and guidance on how to operationalize them. When we examined our crowd-sourced features in the context of predicting stopout, not only were they impressively nuanced, but they also integrated more than one interaction mode between the learner and platform and described how the learner was relatively performing.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"163 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Feature Factory: Crowd Sourced Feature Discovery\",\"authors\":\"K. Veeramachaneni, Kiarash Adl, Una-May O’Reilly\",\"doi\":\"10.1145/2724660.2728696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We examine the process of engineering features for developing models that improve our understanding of learners' online behavior in MOOCs. Because feature engineering relies so heavily on human insight, we engage the crowd for feature proposals and guidance on how to operationalize them. When we examined our crowd-sourced features in the context of predicting stopout, not only were they impressively nuanced, but they also integrated more than one interaction mode between the learner and platform and described how the learner was relatively performing.\",\"PeriodicalId\":20664,\"journal\":{\"name\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"volume\":\"163 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2724660.2728696\",\"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 Second (2015) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2724660.2728696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We examine the process of engineering features for developing models that improve our understanding of learners' online behavior in MOOCs. Because feature engineering relies so heavily on human insight, we engage the crowd for feature proposals and guidance on how to operationalize them. When we examined our crowd-sourced features in the context of predicting stopout, not only were they impressively nuanced, but they also integrated more than one interaction mode between the learner and platform and described how the learner was relatively performing.