{"title":"根据自动题目生成器的作业难度来预测其考试难度","authors":"Binglin Chen, Matthew West, C. Zilles","doi":"10.1145/3330430.3333647","DOIUrl":null,"url":null,"abstract":"To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. In this paper, we study a collection of a few hundred automatic item generators (short computer programs that generate a variety of unique item instances) and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt. This study is conducted with data from introductory undergraduate Computer Science and Mechanical Engineering courses.","PeriodicalId":20693,"journal":{"name":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","volume":"NS30 12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Predicting the difficulty of automatic item generators on exams from their difficulty on homeworks\",\"authors\":\"Binglin Chen, Matthew West, C. Zilles\",\"doi\":\"10.1145/3330430.3333647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. In this paper, we study a collection of a few hundred automatic item generators (short computer programs that generate a variety of unique item instances) and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt. This study is conducted with data from introductory undergraduate Computer Science and Mechanical Engineering courses.\",\"PeriodicalId\":20693,\"journal\":{\"name\":\"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale\",\"volume\":\"NS30 12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3330430.3333647\",\"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 Sixth (2019) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3330430.3333647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting the difficulty of automatic item generators on exams from their difficulty on homeworks
To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. In this paper, we study a collection of a few hundred automatic item generators (short computer programs that generate a variety of unique item instances) and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt. This study is conducted with data from introductory undergraduate Computer Science and Mechanical Engineering courses.