{"title":"基于统一人工免疫系统的远程教育认知技术开发","authors":"G. Samigulina, Z. Samigulina","doi":"10.1109/SIST54437.2022.9945767","DOIUrl":null,"url":null,"abstract":"Current trends in the development of the digital society show the relevance of distance education using innovative artificial intelligence technologies. The need for highly qualified engineering personnel determines the increased role of training students of technical specialties online in higher education institutions. The use of the latest advances in the field of artificial intelligence in education makes it possible to increase the efficiency and quality of student training and optimize the work of teachers with large student flows. The difficulties associated with distance education lie in the need for a differentiated approach to students with different cognitive and psychophysical abilities, social conditions and material and technical base. On the other hand, there are problems with the introduction of these technologies in the educational process and educational programs. The research is devoted to the development of a cognitive technology for distance learning of students of technical specialties using the promising direction of artificial immune systems on the MS Teams platform. It is proposed to use a unified artificial immune system to analyze and predict the efficiency of students' knowledge acquisition, as well as to promptly correct the learning trajectory. The technology has been tested on real data obtained in the process of training students of Kazakh-British Technical University JSC (KBTU).","PeriodicalId":207613,"journal":{"name":"2022 International Conference on Smart Information Systems and Technologies (SIST)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a Distance Education Cognitive Technology Based on a Unified Artificial Immune System\",\"authors\":\"G. Samigulina, Z. Samigulina\",\"doi\":\"10.1109/SIST54437.2022.9945767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current trends in the development of the digital society show the relevance of distance education using innovative artificial intelligence technologies. The need for highly qualified engineering personnel determines the increased role of training students of technical specialties online in higher education institutions. The use of the latest advances in the field of artificial intelligence in education makes it possible to increase the efficiency and quality of student training and optimize the work of teachers with large student flows. The difficulties associated with distance education lie in the need for a differentiated approach to students with different cognitive and psychophysical abilities, social conditions and material and technical base. On the other hand, there are problems with the introduction of these technologies in the educational process and educational programs. The research is devoted to the development of a cognitive technology for distance learning of students of technical specialties using the promising direction of artificial immune systems on the MS Teams platform. It is proposed to use a unified artificial immune system to analyze and predict the efficiency of students' knowledge acquisition, as well as to promptly correct the learning trajectory. The technology has been tested on real data obtained in the process of training students of Kazakh-British Technical University JSC (KBTU).\",\"PeriodicalId\":207613,\"journal\":{\"name\":\"2022 International Conference on Smart Information Systems and Technologies (SIST)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Smart Information Systems and Technologies (SIST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIST54437.2022.9945767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Smart Information Systems and Technologies (SIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIST54437.2022.9945767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of a Distance Education Cognitive Technology Based on a Unified Artificial Immune System
Current trends in the development of the digital society show the relevance of distance education using innovative artificial intelligence technologies. The need for highly qualified engineering personnel determines the increased role of training students of technical specialties online in higher education institutions. The use of the latest advances in the field of artificial intelligence in education makes it possible to increase the efficiency and quality of student training and optimize the work of teachers with large student flows. The difficulties associated with distance education lie in the need for a differentiated approach to students with different cognitive and psychophysical abilities, social conditions and material and technical base. On the other hand, there are problems with the introduction of these technologies in the educational process and educational programs. The research is devoted to the development of a cognitive technology for distance learning of students of technical specialties using the promising direction of artificial immune systems on the MS Teams platform. It is proposed to use a unified artificial immune system to analyze and predict the efficiency of students' knowledge acquisition, as well as to promptly correct the learning trajectory. The technology has been tested on real data obtained in the process of training students of Kazakh-British Technical University JSC (KBTU).