{"title":"自动图像处理技术在科学论证研究中的探索","authors":"Bo Pei, Henglv Zhao, Wanli Xing, Hee-Sun Lee","doi":"10.4018/978-1-5225-9031-6.CH008","DOIUrl":null,"url":null,"abstract":"Scientific argumentation is an epistemic practice where scientific theories are proposed, refined, and refuted, and also a language-based practice where evidence is provided in support of claims. This chapter explores how techniques of computerized image processing can help researchers to identify relationships between features of images and the quality of written artifacts used in scientific argumentation. In this chapter, secondary school students worked in an interactive simulation model and made claims about whether rain water was trapped underground. Automated image processing was employed to precisely quantify several image features relevant to the students' claims. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student's snapshot had a significant effect on that student's claim and explanation scores and the starting and endpoints of the students' lines significantly influenced their explanation scores, but not their claim scores.","PeriodicalId":384539,"journal":{"name":"Cognitive Computing in Technology-Enhanced Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Exploration of Automated Image Processing Techniques in the Study of Scientific Argumentation\",\"authors\":\"Bo Pei, Henglv Zhao, Wanli Xing, Hee-Sun Lee\",\"doi\":\"10.4018/978-1-5225-9031-6.CH008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Scientific argumentation is an epistemic practice where scientific theories are proposed, refined, and refuted, and also a language-based practice where evidence is provided in support of claims. This chapter explores how techniques of computerized image processing can help researchers to identify relationships between features of images and the quality of written artifacts used in scientific argumentation. In this chapter, secondary school students worked in an interactive simulation model and made claims about whether rain water was trapped underground. Automated image processing was employed to precisely quantify several image features relevant to the students' claims. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student's snapshot had a significant effect on that student's claim and explanation scores and the starting and endpoints of the students' lines significantly influenced their explanation scores, but not their claim scores.\",\"PeriodicalId\":384539,\"journal\":{\"name\":\"Cognitive Computing in Technology-Enhanced Learning\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Computing in Technology-Enhanced Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-5225-9031-6.CH008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computing in Technology-Enhanced Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-5225-9031-6.CH008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Exploration of Automated Image Processing Techniques in the Study of Scientific Argumentation
Scientific argumentation is an epistemic practice where scientific theories are proposed, refined, and refuted, and also a language-based practice where evidence is provided in support of claims. This chapter explores how techniques of computerized image processing can help researchers to identify relationships between features of images and the quality of written artifacts used in scientific argumentation. In this chapter, secondary school students worked in an interactive simulation model and made claims about whether rain water was trapped underground. Automated image processing was employed to precisely quantify several image features relevant to the students' claims. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student's snapshot had a significant effect on that student's claim and explanation scores and the starting and endpoints of the students' lines significantly influenced their explanation scores, but not their claim scores.