Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach
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 allo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Online Access: | https://www.mdpi.com/2078-2489/13/8/400 |
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| author | Okan Bulut Damien C. Cormier Seyma Nur Yildirim-Erbasli |
| author_facet | Okan Bulut Damien C. Cormier Seyma Nur Yildirim-Erbasli |
| author_sort | Okan Bulut |
| collection | DOAJ |
| description | 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 (<i>n</i> = 45,478) and 2017–2018 (<i>n</i> = 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, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>, 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. |
| first_indexed | 2024-03-09T04:18:17Z |
| format | Article |
| id | doaj.art-c128775869fe4d188ba55376c8ba74fe |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T04:18:17Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-c128775869fe4d188ba55376c8ba74fe2023-12-03T13:50:58ZengMDPI AGInformation2078-24892022-08-0113840010.3390/info13080400Optimized Screening for At-Risk Students in Mathematics: A Machine Learning ApproachOkan Bulut0Damien C. Cormier1Seyma Nur Yildirim-Erbasli2Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, AB T6G 2G5, CanadaDepartment of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, CanadaDepartment of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, CanadaTraditional 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 (<i>n</i> = 45,478) and 2017–2018 (<i>n</i> = 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, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>, 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.https://www.mdpi.com/2078-2489/13/8/400mathematicsscreeningprogress monitoringcomputerized assessmentmachine learningRandom Forest |
| spellingShingle | Okan Bulut Damien C. Cormier Seyma Nur Yildirim-Erbasli Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach Information mathematics screening progress monitoring computerized assessment machine learning Random Forest |
| title | Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach |
| title_full | Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach |
| title_fullStr | Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach |
| title_full_unstemmed | Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach |
| title_short | Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach |
| title_sort | optimized screening for at risk students in mathematics a machine learning approach |
| topic | mathematics screening progress monitoring computerized assessment machine learning Random Forest |
| url | https://www.mdpi.com/2078-2489/13/8/400 |
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