Identifying false positives when targeting students at risk of dropping out

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision...

Full description

Saved in:
Bibliographic Details
Published in:Education economics 2023-05, Vol.31 (3), p.313-325
Main Authors: Eegdeman, Irene, Cornelisz, Ilja, Meeter, Martijn, van Klaveren, Chris
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
At Risk Students
Dropout Characteristics
Dropout Prevention
Dropping out
Foreign Countries
Identification
Intervention
Learning
machine learning
Methods
Prediction
Prevention
Study success
Vocational education
Vocational Schools
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for dropout prevention explicit. Data of a Dutch vocational education institute is used to show how out-of-sample machine learning predictions can be used to formulate invitation rules in a way that targets students at risk more effectively, thereby facilitating early detection for effective dropout prevention.
ISSN:0964-5292
1469-5782
DOI:10.1080/09645292.2022.2067131