The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction
Sunbok Lee; Jae Young Chung · 2019 · Applied Sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling t
Abstract by Sunbok Lee; Jae Young Chung, Applied Sciences (2019) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/app9153093