K. Koile, Andee Rubin, S. Chapman, M. Kliman, Lily Ko
{"title":"Using machine analysis to make elementary students' mathematical thinking visible","authors":"K. Koile, Andee Rubin, S. Chapman, M. Kliman, Lily Ko","doi":"10.1145/2883851.2883922","DOIUrl":null,"url":null,"abstract":"The INK-12: Teaching and Learning Using Interactive Ink Inscriptions in K-12 project has been developing and investigating the use of pen-based technology in elementary math classes. This paper reports on progress made on machine analysis of students' visual representations created using digital tools developed to support learning multiplication and division. The goal of the analysis is to make student thinking visible in order to (a) better understand how students learn multiplication and division, and (b) provide feedback to teachers, e.g., about strategies students use to solve problems. Student work from a five-week trial in a third grade class provides a corpus for development and evaluation of the machine analysis routines. Preliminary findings indicate that the routines can reproduce human analyses.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2883851.2883922","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The INK-12: Teaching and Learning Using Interactive Ink Inscriptions in K-12 project has been developing and investigating the use of pen-based technology in elementary math classes. This paper reports on progress made on machine analysis of students' visual representations created using digital tools developed to support learning multiplication and division. The goal of the analysis is to make student thinking visible in order to (a) better understand how students learn multiplication and division, and (b) provide feedback to teachers, e.g., about strategies students use to solve problems. Student work from a five-week trial in a third grade class provides a corpus for development and evaluation of the machine analysis routines. Preliminary findings indicate that the routines can reproduce human analyses.