L. Galhardi, C. R. Barbosa, Rodrigo Souza, J. Brancher
{"title":"葡萄牙语自动简答评分","authors":"L. Galhardi, C. R. Barbosa, Rodrigo Souza, J. Brancher","doi":"10.5753/CBIE.SBIE.2018.1373","DOIUrl":null,"url":null,"abstract":"Automatic Short Answer Grading is the study field that addresses the assessment of students’ answers to questions in natural language. Besides length, it differs from automatic essay grading by focusing on the evaluation of content instead of answer’s style. The grading of the answers is generally seen as a typical classification supervised learning. Many works have been recently developed, but most of them deal with data in the English language. In this paper, we present a new Portuguese dataset and system for automatic short answer grading. The data was collected with the participation of 13 teachers, 12 undergraduate students and 245 elementary school students. Results achieved 69% accuracy in four-class classification and 85% on binary classification.","PeriodicalId":231173,"journal":{"name":"Anais do XXIX Simpósio Brasileiro de Informática na Educação (SBIE 2018)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Portuguese Automatic Short Answer Grading\",\"authors\":\"L. Galhardi, C. R. Barbosa, Rodrigo Souza, J. Brancher\",\"doi\":\"10.5753/CBIE.SBIE.2018.1373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic Short Answer Grading is the study field that addresses the assessment of students’ answers to questions in natural language. Besides length, it differs from automatic essay grading by focusing on the evaluation of content instead of answer’s style. The grading of the answers is generally seen as a typical classification supervised learning. Many works have been recently developed, but most of them deal with data in the English language. In this paper, we present a new Portuguese dataset and system for automatic short answer grading. The data was collected with the participation of 13 teachers, 12 undergraduate students and 245 elementary school students. Results achieved 69% accuracy in four-class classification and 85% on binary classification.\",\"PeriodicalId\":231173,\"journal\":{\"name\":\"Anais do XXIX Simpósio Brasileiro de Informática na Educação (SBIE 2018)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anais do XXIX Simpósio Brasileiro de Informática na Educação (SBIE 2018)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5753/CBIE.SBIE.2018.1373\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do XXIX Simpósio Brasileiro de Informática na Educação (SBIE 2018)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/CBIE.SBIE.2018.1373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Short Answer Grading is the study field that addresses the assessment of students’ answers to questions in natural language. Besides length, it differs from automatic essay grading by focusing on the evaluation of content instead of answer’s style. The grading of the answers is generally seen as a typical classification supervised learning. Many works have been recently developed, but most of them deal with data in the English language. In this paper, we present a new Portuguese dataset and system for automatic short answer grading. The data was collected with the participation of 13 teachers, 12 undergraduate students and 245 elementary school students. Results achieved 69% accuracy in four-class classification and 85% on binary classification.