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  • 标题:Exploring Student Academic Performance Using Data Mining Tools
  • 本地全文:下载
  • 作者:Ranjit Paul ; Silvia Gaftandzhieva ; Samina Kausar
  • 期刊名称:International Journal of Emerging Technologies in Learning (iJET)
  • 印刷版ISSN:1863-0383
  • 出版年度:2020
  • 卷号:15
  • 期号:08
  • 页码:195-209
  • DOI:10.3991/ijet.v15i08.12557
  • 出版社:Kassel University Press
  • 摘要:Most of the educational institutes nowadays benefited from the hidden knowledge extracted from the datasets of their students, instructors and educational settings. The education system has gone through a paradigm shift from a traditional system to smart learning environments and from a teacher-centric system to context-aware any time anywhere student-centric approach. In this changing scenario, we have undertaken a study to investigate the results, grades and patterns of the students of North Lakhimpur College. The paper aims to evaluate the quality of learning on the basis of 19249 grades received from 758 students in 511 courses, included in the curriculum of 3 study programmes.
  • 关键词:datasets; quality evaluation; data mining; student academic performance; educational data mining
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