{"title":"vi - r:使用近因性来改进学生模型和领域模型","authors":"Ilya M. Goldin, April Galyardt","doi":"10.1145/2724660.2728706","DOIUrl":null,"url":null,"abstract":"We describe a new method to troubleshoot and improve domain and student models from interactive learning environments. The method applies as long as the models can generate predictions of student behavior. The method is a visualization of model predictions, categorized using a metric of recent performance. We describe the method, its application in prior work to student models, and a proposed extension to domain models.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"260 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Viz-R: Using Recency to Improve Student and Domain Models\",\"authors\":\"Ilya M. Goldin, April Galyardt\",\"doi\":\"10.1145/2724660.2728706\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a new method to troubleshoot and improve domain and student models from interactive learning environments. The method applies as long as the models can generate predictions of student behavior. The method is a visualization of model predictions, categorized using a metric of recent performance. We describe the method, its application in prior work to student models, and a proposed extension to domain models.\",\"PeriodicalId\":20664,\"journal\":{\"name\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"volume\":\"260 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2724660.2728706\",\"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/10.1145/2724660.2728706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Viz-R: Using Recency to Improve Student and Domain Models
We describe a new method to troubleshoot and improve domain and student models from interactive learning environments. The method applies as long as the models can generate predictions of student behavior. The method is a visualization of model predictions, categorized using a metric of recent performance. We describe the method, its application in prior work to student models, and a proposed extension to domain models.