{"title":"预测失败:合作博客的案例研究","authors":"Bjorn Levi Gunnarsson, R. Alterman","doi":"10.1145/2330601.2330665","DOIUrl":null,"url":null,"abstract":"Monitoring student progress in homework is important but difficult to do. The work in this paper presents a method for monitoring student progress based on their participation. By tracking participation we can successfully create a model that predicts, with very high accuracy, if a student is going to score a low grade on her current assignment before it is completed, thus enabling selective interventions.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Predicting failure: a case study in co-blogging\",\"authors\":\"Bjorn Levi Gunnarsson, R. Alterman\",\"doi\":\"10.1145/2330601.2330665\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Monitoring student progress in homework is important but difficult to do. The work in this paper presents a method for monitoring student progress based on their participation. By tracking participation we can successfully create a model that predicts, with very high accuracy, if a student is going to score a low grade on her current assignment before it is completed, thus enabling selective interventions.\",\"PeriodicalId\":311750,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2330601.2330665\",\"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 2nd International Conference on Learning Analytics and Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2330601.2330665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monitoring student progress in homework is important but difficult to do. The work in this paper presents a method for monitoring student progress based on their participation. By tracking participation we can successfully create a model that predicts, with very high accuracy, if a student is going to score a low grade on her current assignment before it is completed, thus enabling selective interventions.