Advancing High School Dropout Predictions Using Machine Learning

被引:0
|
作者
Alam, Anika [1 ]
Bowden, A. Brooks [1 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
来源
ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM AND BLUE SKY, AIED 2024 | 2024年 / 2151卷
关键词
Prediction models; machine learning; early warning systems; GRADUATION; STUDENTS; GRADES; SMOTE;
D O I
10.1007/978-3-031-64312-5_40
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The importance of high school completion for jobs and postsecondary opportunities is well-documented. Schools, districts, and states are increasingly concerned about improving outcomes for vulnerable student populations. Combined with U.S. federal laws where high school graduation rate is a core performance indicator, states face pressure to actively monitor and assess high school completion. This study employs machine learning algorithms to preemptively identify students at-risk of dropping out of high school. We leverage North Carolina statewide administrative data to build an early warning prediction model that identifies students at-risk of dropping out and groups them based on similar patterns and characteristics. This study provides guidance and informed knowledge about how districts and states can capitalize "big data" to identify vulnerable students preemptively.
引用
收藏
页码:334 / 341
页数:8
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