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How the Predictors of Math Achievement Change over Time: A Longitudinal Machine Learning Approach

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Title: How the Predictors of Math Achievement Change over Time: A Longitudinal Machine Learning Approach
Language: English
Authors: Rosa Lavelle-Hill (ORCID 0000-0002-1767-9828), Anne C. Frenzel, Thomas Goetz, Stephanie Lichtenfeld, Herbert W. Marsh, Reinhard Pekrun, Michiko Sakaki, Gavin Smith, Kou Murayama
Source: Journal of Educational Psychology. 2024 116(8):1383-1403.
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
Peer Reviewed: Y
Page Count: 21
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Mathematics Achievement, Predictor Variables, Artificial Intelligence, Secondary School Students, Intelligence Quotient, Grades (Scholastic), Student Motivation, Cognitive Processes, Family Environment, Educational Environment, Socioeconomic Status, Student Characteristics, Foreign Countries
Geographic Terms: Germany
DOI: 10.1037/edu0000863
ISSN: 0022-0663
1939-2176
Abstract: Researchers have focused extensively on understanding the factors influencing students' academic achievement over time. However, existing longitudinal studies have often examined only a limited number of predictors at one time, leaving gaps in our knowledge about how these predictors collectively contribute to achievement beyond prior performance and how their impact evolves during students' development. To address this, we employed machine learning to analyze longitudinal survey data from 3,425 German secondary school students spanning 5 to 9 years. Our objectives were twofold: to model and compare the predictive capabilities of 105 predictors on math achievement and to track changes in their importance over time. We first predicted standardized math achievement scores in Years 6-9 using the variables assessed in the previous year ("next year prediction"). Second, we examined the utility of the variables assessed in Year 5 at predicting future math achievement at varying time lags (1-4 years ahead)--"varying lag prediction." In the next year prediction analysis, prior math achievement was the strongest predictor, gaining importance over time. In the varying lag prediction analysis, the predictive power of Year 5 math achievement waned with longer time lags. In both analyses, additional predictors, including intelligence quotient, grades, motivation and emotion, cognitive strategies, classroom/home environments, and demographics (including socioeconomic status), exhibited relatively smaller yet consistent contributions, underscoring their distinct roles in predicting math achievement over time. The findings have implications for both future research and educational practices, which are discussed in detail.
Abstractor: As Provided
Notes: https://github.com/Rosa-Lavelle-Hill/palma-ml-open
Entry Date: 2026
Accession Number: EJ1506533
Database: ERIC
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  Data: How the Predictors of Math Achievement Change over Time: A Longitudinal Machine Learning Approach
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  Data: <searchLink fieldCode="AR" term="%22Rosa+Lavelle-Hill%22">Rosa Lavelle-Hill</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1767-9828">0000-0002-1767-9828</externalLink>)<br /><searchLink fieldCode="AR" term="%22Anne+C%2E+Frenzel%22">Anne C. Frenzel</searchLink><br /><searchLink fieldCode="AR" term="%22Thomas+Goetz%22">Thomas Goetz</searchLink><br /><searchLink fieldCode="AR" term="%22Stephanie+Lichtenfeld%22">Stephanie Lichtenfeld</searchLink><br /><searchLink fieldCode="AR" term="%22Herbert+W%2E+Marsh%22">Herbert W. Marsh</searchLink><br /><searchLink fieldCode="AR" term="%22Reinhard+Pekrun%22">Reinhard Pekrun</searchLink><br /><searchLink fieldCode="AR" term="%22Michiko+Sakaki%22">Michiko Sakaki</searchLink><br /><searchLink fieldCode="AR" term="%22Gavin+Smith%22">Gavin Smith</searchLink><br /><searchLink fieldCode="AR" term="%22Kou+Murayama%22">Kou Murayama</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Psychology%22"><i>Journal of Educational Psychology</i></searchLink>. 2024 116(8):1383-1403.
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  Data: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
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  Data: 21
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+Quotient%22">Intelligence Quotient</searchLink><br /><searchLink fieldCode="DE" term="%22Grades+%28Scholastic%29%22">Grades (Scholastic)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Environment%22">Family Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Environment%22">Educational Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Status%22">Socioeconomic Status</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
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  Data: 10.1037/edu0000863
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  Data: 0022-0663<br />1939-2176
– Name: Abstract
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  Data: Researchers have focused extensively on understanding the factors influencing students' academic achievement over time. However, existing longitudinal studies have often examined only a limited number of predictors at one time, leaving gaps in our knowledge about how these predictors collectively contribute to achievement beyond prior performance and how their impact evolves during students' development. To address this, we employed machine learning to analyze longitudinal survey data from 3,425 German secondary school students spanning 5 to 9 years. Our objectives were twofold: to model and compare the predictive capabilities of 105 predictors on math achievement and to track changes in their importance over time. We first predicted standardized math achievement scores in Years 6-9 using the variables assessed in the previous year ("next year prediction"). Second, we examined the utility of the variables assessed in Year 5 at predicting future math achievement at varying time lags (1-4 years ahead)--"varying lag prediction." In the next year prediction analysis, prior math achievement was the strongest predictor, gaining importance over time. In the varying lag prediction analysis, the predictive power of Year 5 math achievement waned with longer time lags. In both analyses, additional predictors, including intelligence quotient, grades, motivation and emotion, cognitive strategies, classroom/home environments, and demographics (including socioeconomic status), exhibited relatively smaller yet consistent contributions, underscoring their distinct roles in predicting math achievement over time. The findings have implications for both future research and educational practices, which are discussed in detail.
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  Label: Abstractor
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  Data: As Provided
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  Data: https://github.com/Rosa-Lavelle-Hill/palma-ml-open
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  Data: 2026
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  Label: Accession Number
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  Data: EJ1506533
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1506533
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        Value: 10.1037/edu0000863
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      – SubjectFull: Mathematics Achievement
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Artificial Intelligence
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