{"title":"基于教育大数据的高校定制化教育探讨:高校定制化教育","authors":"Huiying Cai","doi":"10.1145/3488466.3488493","DOIUrl":null,"url":null,"abstract":"With the improvement of network, online teaching has gradually become an important auxiliary teaching mean. In the whole process of teaching and learning, a large amount of educational data is produced. Educational data contain a lot of information to be mined, which is helpful to improve the quality of learning and teaching. This paper will explore how to integrate these educational data that comes from different educational platform to guide customizable education which refers to determine the learning or teaching content independently. To realize the customizable education, the evaluation indicators and detail scheme to make use of the educational big data are proposed. The scheme consists of the acquisition, analysis and visualization of the data for different evaluation indicators. The purpose of this paper is to make these data guide undergraduates to carry out targeted autonomous learning according to their states to promote the learning progress. It can also guide teachers to carry out targeted teaching activities to improve teaching quality. And it is also helpful for teaching managers to have an insight into the learning state of students and the teaching state of the teachers, so as to put forward more reasonable teaching plans and countermeasures. This paper provided a whole framework for the application of the educational big data which can be extended by different educational institutions.","PeriodicalId":196340,"journal":{"name":"Proceedings of the 5th International Conference on Digital Technology in Education","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discussion on Customizable Education of Colleges Based on Educational Big Data: Customizable Education of Colleges\",\"authors\":\"Huiying Cai\",\"doi\":\"10.1145/3488466.3488493\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the improvement of network, online teaching has gradually become an important auxiliary teaching mean. In the whole process of teaching and learning, a large amount of educational data is produced. Educational data contain a lot of information to be mined, which is helpful to improve the quality of learning and teaching. This paper will explore how to integrate these educational data that comes from different educational platform to guide customizable education which refers to determine the learning or teaching content independently. To realize the customizable education, the evaluation indicators and detail scheme to make use of the educational big data are proposed. The scheme consists of the acquisition, analysis and visualization of the data for different evaluation indicators. The purpose of this paper is to make these data guide undergraduates to carry out targeted autonomous learning according to their states to promote the learning progress. It can also guide teachers to carry out targeted teaching activities to improve teaching quality. And it is also helpful for teaching managers to have an insight into the learning state of students and the teaching state of the teachers, so as to put forward more reasonable teaching plans and countermeasures. This paper provided a whole framework for the application of the educational big data which can be extended by different educational institutions.\",\"PeriodicalId\":196340,\"journal\":{\"name\":\"Proceedings of the 5th International Conference on Digital Technology in Education\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th International Conference on Digital Technology in Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3488466.3488493\",\"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 5th International Conference on Digital Technology in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3488466.3488493","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Discussion on Customizable Education of Colleges Based on Educational Big Data: Customizable Education of Colleges
With the improvement of network, online teaching has gradually become an important auxiliary teaching mean. In the whole process of teaching and learning, a large amount of educational data is produced. Educational data contain a lot of information to be mined, which is helpful to improve the quality of learning and teaching. This paper will explore how to integrate these educational data that comes from different educational platform to guide customizable education which refers to determine the learning or teaching content independently. To realize the customizable education, the evaluation indicators and detail scheme to make use of the educational big data are proposed. The scheme consists of the acquisition, analysis and visualization of the data for different evaluation indicators. The purpose of this paper is to make these data guide undergraduates to carry out targeted autonomous learning according to their states to promote the learning progress. It can also guide teachers to carry out targeted teaching activities to improve teaching quality. And it is also helpful for teaching managers to have an insight into the learning state of students and the teaching state of the teachers, so as to put forward more reasonable teaching plans and countermeasures. This paper provided a whole framework for the application of the educational big data which can be extended by different educational institutions.