{"title":"机器学习在教育领域的趋势、前景和前景","authors":"N. Jalil, H. Hwang, N. M. Dawi","doi":"10.1145/3345120.3345147","DOIUrl":null,"url":null,"abstract":"In the contemporary exam-driven domain of education, each time a new technology transpires, societies want to know how it can be used to make kids get superior grades, how it can expedite teaching and cut the expenditure of learning, and could it be used to substitute teachers altogether? For a considerable length of time, training technophiles have imagined a future wherein gee-whiz gadgets and drawing in advanced applications whisk students from the stagnations of conventional study hall guidance and into a fun universe of signaling PCs, self-managed exercises, and cloud-based coordinated effort. Machine learning can possibly strengthen parts of educating and learning that are as of now tedious and hard to oversee. Machine learning is tremendously affecting the education industry. Moving forward into year 2020, it is not the technology itself that needs to change. In most aspects of our lives, technology has made significant changes for good and bad, but in education, predominantly schools and universities, there is still persistent resistance. Subsequently, students were compelled to attempt to alter their style of learning to the exercise plan, instead of a different way. As society eyes, arranged innovation with both fervor and doubt, universities the nation over are developing frameworks that gather and examine immense measures of understudy information to foresee and reinforce understudy achievement and achieve other institutional objectives.","PeriodicalId":187197,"journal":{"name":"Proceedings of the 3rd International Conference on Education and Multimedia Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Machines Learning Trends, Perspectives and Prospects in Education Sector\",\"authors\":\"N. Jalil, H. Hwang, N. M. Dawi\",\"doi\":\"10.1145/3345120.3345147\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the contemporary exam-driven domain of education, each time a new technology transpires, societies want to know how it can be used to make kids get superior grades, how it can expedite teaching and cut the expenditure of learning, and could it be used to substitute teachers altogether? For a considerable length of time, training technophiles have imagined a future wherein gee-whiz gadgets and drawing in advanced applications whisk students from the stagnations of conventional study hall guidance and into a fun universe of signaling PCs, self-managed exercises, and cloud-based coordinated effort. Machine learning can possibly strengthen parts of educating and learning that are as of now tedious and hard to oversee. Machine learning is tremendously affecting the education industry. Moving forward into year 2020, it is not the technology itself that needs to change. In most aspects of our lives, technology has made significant changes for good and bad, but in education, predominantly schools and universities, there is still persistent resistance. Subsequently, students were compelled to attempt to alter their style of learning to the exercise plan, instead of a different way. As society eyes, arranged innovation with both fervor and doubt, universities the nation over are developing frameworks that gather and examine immense measures of understudy information to foresee and reinforce understudy achievement and achieve other institutional objectives.\",\"PeriodicalId\":187197,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Education and Multimedia Technology\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Education and Multimedia Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3345120.3345147\",\"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 3rd International Conference on Education and Multimedia Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3345120.3345147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machines Learning Trends, Perspectives and Prospects in Education Sector
In the contemporary exam-driven domain of education, each time a new technology transpires, societies want to know how it can be used to make kids get superior grades, how it can expedite teaching and cut the expenditure of learning, and could it be used to substitute teachers altogether? For a considerable length of time, training technophiles have imagined a future wherein gee-whiz gadgets and drawing in advanced applications whisk students from the stagnations of conventional study hall guidance and into a fun universe of signaling PCs, self-managed exercises, and cloud-based coordinated effort. Machine learning can possibly strengthen parts of educating and learning that are as of now tedious and hard to oversee. Machine learning is tremendously affecting the education industry. Moving forward into year 2020, it is not the technology itself that needs to change. In most aspects of our lives, technology has made significant changes for good and bad, but in education, predominantly schools and universities, there is still persistent resistance. Subsequently, students were compelled to attempt to alter their style of learning to the exercise plan, instead of a different way. As society eyes, arranged innovation with both fervor and doubt, universities the nation over are developing frameworks that gather and examine immense measures of understudy information to foresee and reinforce understudy achievement and achieve other institutional objectives.