Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach
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From the abstract; "Traditional screening approaches identify students who might be at risk for academic
problems based on how they perform on a single screening measure. However, using multiple
screening measures may improve accuracy when identifying at-risk students. The advent of machine
learning algorithms has allowed researchers to consider using advanced predictive models to identify
at-risk students. The purpose of this study is to investigate if machine learning algorithms can
strengthen the accuracy of predictions made from progress monitoring data to classify students as
at risk for low mathematics performance. This study used a sample of first-grade students who
completed a series of computerized formative assessments (Star Math, Star Reading, and Star Early
Literacy) during the 2016-2017 (n = 45,478) and 2017-2018 (n = 45,501) school years. Predictive models
using two machine learning algorithms (i.e., Random Forest and LogitBoost) were constructed to
identify students at risk for low mathematics performance. The classification results were evaluated
using evaluation metrics of accuracy, sensitivity, specificity, F1, and Matthews correlation coefficient."
Across the five metrics, a multi-measure screening procedure involving mathematics, reading, and
early literacy scores generally outperformed single-measure approaches relying solely on mathematics
scores. These findings suggest that educators may be able to use a cluster of measures administered
once at the beginning of the school year to screen their first grade for at-risk math performance."
Citation: Bulut, O., Cormier, D. C., & Yildirim-Erbasli, S. N. (2022). Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach. Information, 13(8), 400.
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