{"title":"Problems Before Solutions: Automated Problem Clarification at Scale","authors":"S. Basu, A. Wu, Brian Hou, John DeNero","doi":"10.1145/2724660.2724679","DOIUrl":null,"url":null,"abstract":"Automatic assessment reduces the need for individual feedback in massive courses, but often focuses only on scoring solutions, rather than assessing whether students correctly understand problems. We present an enriched approach to automatic assessment that explicitly assists students in understanding the detailed specification of technical problems that they are asked to solve, in addition to evaluating their solutions. Students are given a suite of solution test cases, but they must first unlock each test case by validating its behavior before they are allowed to apply it to their proposed solution. When provided with this automated feedback early in the problem-solving process, students ask fewer clarificatory questions and express less confusion about assessments. As a result, instructors spend less time explaining problems to students. In a 1300-person university course, we observed that the vast majority of students chose to validate their understanding of test cases before attempting to solve problems. These students reported that the validation process improved their understanding.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"13 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2724660.2724679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Automatic assessment reduces the need for individual feedback in massive courses, but often focuses only on scoring solutions, rather than assessing whether students correctly understand problems. We present an enriched approach to automatic assessment that explicitly assists students in understanding the detailed specification of technical problems that they are asked to solve, in addition to evaluating their solutions. Students are given a suite of solution test cases, but they must first unlock each test case by validating its behavior before they are allowed to apply it to their proposed solution. When provided with this automated feedback early in the problem-solving process, students ask fewer clarificatory questions and express less confusion about assessments. As a result, instructors spend less time explaining problems to students. In a 1300-person university course, we observed that the vast majority of students chose to validate their understanding of test cases before attempting to solve problems. These students reported that the validation process improved their understanding.