{"title":"新手程序员使用IDE的哪些方面可以预测学习结果","authors":"G. Dyke","doi":"10.1145/1953163.1953309","DOIUrl":null,"url":null,"abstract":"We present the preliminary analysis of a study whose long term aim is to track IDE usage to identify novice-programmers in need of support. Our analysis focused on the activity of 24 dyads on a 3 week assignment. We correlated frequencies of events such as use of code generation and of the debugger with assignment grades, final exam grades, and the difference in rankings within dyad on the final exam. Our results show several significant correlations. In particular, code generation and debugging are correlated with the final grade, and running in non-debug mode is correlated with differences in ranking. These results are encouraging as they show that it is possible to predict learning outcomes with simple frequency data and suggest more complex indicators could achieve robust prediction.","PeriodicalId":137934,"journal":{"name":"Proceedings of the 42nd ACM technical symposium on Computer science education","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Which aspects of novice programmers' usage of an IDE predict learning outcomes\",\"authors\":\"G. Dyke\",\"doi\":\"10.1145/1953163.1953309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the preliminary analysis of a study whose long term aim is to track IDE usage to identify novice-programmers in need of support. Our analysis focused on the activity of 24 dyads on a 3 week assignment. We correlated frequencies of events such as use of code generation and of the debugger with assignment grades, final exam grades, and the difference in rankings within dyad on the final exam. Our results show several significant correlations. In particular, code generation and debugging are correlated with the final grade, and running in non-debug mode is correlated with differences in ranking. These results are encouraging as they show that it is possible to predict learning outcomes with simple frequency data and suggest more complex indicators could achieve robust prediction.\",\"PeriodicalId\":137934,\"journal\":{\"name\":\"Proceedings of the 42nd ACM technical symposium on Computer science education\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 42nd ACM technical symposium on Computer science education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1953163.1953309\",\"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 42nd ACM technical symposium on Computer science education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1953163.1953309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Which aspects of novice programmers' usage of an IDE predict learning outcomes
We present the preliminary analysis of a study whose long term aim is to track IDE usage to identify novice-programmers in need of support. Our analysis focused on the activity of 24 dyads on a 3 week assignment. We correlated frequencies of events such as use of code generation and of the debugger with assignment grades, final exam grades, and the difference in rankings within dyad on the final exam. Our results show several significant correlations. In particular, code generation and debugging are correlated with the final grade, and running in non-debug mode is correlated with differences in ranking. These results are encouraging as they show that it is possible to predict learning outcomes with simple frequency data and suggest more complex indicators could achieve robust prediction.