{"title":"Big Data Analytics","authors":"P. Guleria, M. Sood","doi":"10.4018/978-1-5225-6210-8.CH004","DOIUrl":null,"url":null,"abstract":"Due to an increase in the number of digital transactions and data sources, a huge amount of unstructured data is generated by every interaction. In such a scenario, the concepts of data mining assume great significance as useful information/trends/predictions can be retrieved from this large amount of data, known as big data. Big data predictive analytics are making big inroads into the educational field because with the adoption of new technologies, new academic trends are being introduced into educational systems. This accumulation of large data of different varieties throws a new set of challenges to the learners as well as educational institutions in ensuring the quality of their education by improving strategic/operational decision-making capabilities. Therefore, the authors address this issue by proposing a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences. This chapter provides the readers with the requisite information about educational frameworks and related data mining.","PeriodicalId":365327,"journal":{"name":"Predictive Intelligence Using Big Data and the Internet of Things","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":"Predictive Intelligence Using Big Data and the Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-5225-6210-8.CH004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Due to an increase in the number of digital transactions and data sources, a huge amount of unstructured data is generated by every interaction. In such a scenario, the concepts of data mining assume great significance as useful information/trends/predictions can be retrieved from this large amount of data, known as big data. Big data predictive analytics are making big inroads into the educational field because with the adoption of new technologies, new academic trends are being introduced into educational systems. This accumulation of large data of different varieties throws a new set of challenges to the learners as well as educational institutions in ensuring the quality of their education by improving strategic/operational decision-making capabilities. Therefore, the authors address this issue by proposing a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences. This chapter provides the readers with the requisite information about educational frameworks and related data mining.
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