Analytics for Good: Predicting Secondary Education Attainment with Text Analytics

被引:0
|
作者
Powers, Jonathan [1 ]
Bergman, Shawn [1 ]
Cazier, Joseph [1 ]
机构
[1] Appalachian State Univ, Ctr Analyt Res & Educ, Boone, NC 28608 USA
来源
关键词
Text analytics; Educational attainment; interventions; predictive modeling; SCHOOL ENGAGEMENT;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Factors influencing secondary educational attainment (staying in high school) are well characterized, but several shortcomings of current educational attainment theory preclude a real-time and wide-scope understanding of the issue. Further insight could be provided by text analytic technology. In this article, the authors review the current status of secondary educational attainment and its associated theories, and identify areas of weakness which could be reinforced by adopting text analytic research methods. The authors conclude with a discussion of potential applications of text analytic research within school systems aimed at equipping educators and counselors with additional resources to more accurately pinpoint intervention efforts.
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页数:6
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