{"title":"半自动生成智能课程以促进学习分析","authors":"Angel Fiallos, X. Ochoa","doi":"10.1145/3303772.3303834","DOIUrl":null,"url":null,"abstract":"Several Learning Analytics applications are limited by the cost of generating a computer understandable description of the course domain, what is called an Intelligent Curriculum. The following work contributes a novel approach to (semi-)automatically generate Intelligent Curriculum through ontologies extracted from existing learning materials such as digital books or web content. Through a series of natural language processing steps, the semi-structured information present in existing content is transformed into a concept-graph. This work also evaluates the proposed methodology by applying it to learning content for two different courses and measuring the quality of the extracted ontologies against manually generated ones. The results obtained suggest that the technique can be readily used to provide domain information to other Learning Analytics tools.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Semi-Automatic Generation of Intelligent Curricula to Facilitate Learning Analytics\",\"authors\":\"Angel Fiallos, X. Ochoa\",\"doi\":\"10.1145/3303772.3303834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several Learning Analytics applications are limited by the cost of generating a computer understandable description of the course domain, what is called an Intelligent Curriculum. The following work contributes a novel approach to (semi-)automatically generate Intelligent Curriculum through ontologies extracted from existing learning materials such as digital books or web content. Through a series of natural language processing steps, the semi-structured information present in existing content is transformed into a concept-graph. This work also evaluates the proposed methodology by applying it to learning content for two different courses and measuring the quality of the extracted ontologies against manually generated ones. The results obtained suggest that the technique can be readily used to provide domain information to other Learning Analytics tools.\",\"PeriodicalId\":382957,\"journal\":{\"name\":\"Proceedings of the 9th International Conference on Learning Analytics & Knowledge\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th International Conference on Learning Analytics & Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3303772.3303834\",\"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 9th International Conference on Learning Analytics & Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3303772.3303834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Semi-Automatic Generation of Intelligent Curricula to Facilitate Learning Analytics
Several Learning Analytics applications are limited by the cost of generating a computer understandable description of the course domain, what is called an Intelligent Curriculum. The following work contributes a novel approach to (semi-)automatically generate Intelligent Curriculum through ontologies extracted from existing learning materials such as digital books or web content. Through a series of natural language processing steps, the semi-structured information present in existing content is transformed into a concept-graph. This work also evaluates the proposed methodology by applying it to learning content for two different courses and measuring the quality of the extracted ontologies against manually generated ones. The results obtained suggest that the technique can be readily used to provide domain information to other Learning Analytics tools.