Assessing reading fluency in elementary grades: A machine learning approach
This study compares eleven widely used machine learning algorithms to identify the most accurate and comprehensive method for assessing children's reading fluency. Our fluency framework integrates three key dimensions: accuracy in word decoding, reading speed (words per minute), and prosody, wh...
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| Format: | Article |
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Elsevier
2025-06-01
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| Series: | Computers and Education: Artificial Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X25000517 |
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| author | Gabriel Candido da Silva Rodrigo Lins Rodrigues Américo N. Amorim Lieny Jeon Emilia X.S. Albuquerque Vanessa C. Silva Vinícius F. da Silva André L.A. Pinheiro João P.J.R. Nunes Suzana X.M.G. de Souza Maxsuel S. Silva Igor Mauro Alexandre Magno Andrade Maciel |
| author_facet | Gabriel Candido da Silva Rodrigo Lins Rodrigues Américo N. Amorim Lieny Jeon Emilia X.S. Albuquerque Vanessa C. Silva Vinícius F. da Silva André L.A. Pinheiro João P.J.R. Nunes Suzana X.M.G. de Souza Maxsuel S. Silva Igor Mauro Alexandre Magno Andrade Maciel |
| author_sort | Gabriel Candido da Silva |
| collection | DOAJ |
| description | This study compares eleven widely used machine learning algorithms to identify the most accurate and comprehensive method for assessing children's reading fluency. Our fluency framework integrates three key dimensions: accuracy in word decoding, reading speed (words per minute), and prosody, which captures appropriate pausing and intonation during reading. Audio recordings from 2nd and 3rd grade students across 144 Brazilian schools were transcribed using the advanced Whisper ASR system, enabling automated extraction of fluency features. The research objective was to determine which algorithm best predicts fluency, considering diverse evaluation setups including binary classification (fluent versus non-fluent), multiclass classification (differentiated fluency levels), and regression analysis to estimate continuous fluency scores. Among the eleven models evaluated, Logistic Regression achieved the highest overall performance in the classification experiments, demonstrating superior precision and accuracy. Ensemble methods such as Gradient Boosting and Random Forest also yielded robust results, particularly in regression analyses where they effectively captured the variability in expert fluency ratings. Notably, reading speed emerged as the most critical indicator of fluency across all experiments, consistently outweighing contributions from accuracy and prosody. Nevertheless, prosody maintained significant importance, especially when fluency was modeled as a continuous variable, emphasizing the value of expressive reading. These findings suggest that while high accuracy in word decoding is fundamental, interventions that enhance reading speed and incorporate prosodic features can offer more nuanced support for reading development. This work provides educators with insights into scalable, automated, and cost-effective approaches to monitoring and improving reading fluency. |
| format | Article |
| id | doaj-art-12d6ca9acee14064814d13f054e8de8c |
| institution | Kabale University |
| issn | 2666-920X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-12d6ca9acee14064814d13f054e8de8c2025-08-20T03:47:10ZengElsevierComputers and Education: Artificial Intelligence2666-920X2025-06-01810041110.1016/j.caeai.2025.100411Assessing reading fluency in elementary grades: A machine learning approachGabriel Candido da Silva0Rodrigo Lins Rodrigues1Américo N. Amorim2Lieny Jeon3Emilia X.S. Albuquerque4Vanessa C. Silva5Vinícius F. da Silva6André L.A. Pinheiro7João P.J.R. Nunes8Suzana X.M.G. de Souza9Maxsuel S. Silva10Igor Mauro11Alexandre Magno Andrade Maciel12Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Rua Benfica, 455, Recife, 50720-001, PE, Brazil; Corresponding author.Programa de Pós-Graduação em Ensino das Ciências, Universidade Federal Rural de Pernambuco, Avenida Dom Manoel de Medeiros, s/n, Recife, 52171-900, PE, BrazilCenter for Faculty Advancement, New York University, 82 Washington Square E, New York, 10003, NY, United States; Escribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, Brazil; Inteligência Artificial na Educação e Aprendizado (IAEA), CESAR School, Avenida Cais do Apolo, 77, Recife, 50030-220, PE, BrazilSchool of Education and Human Development, University of Virginia, 405 Emmet St S, Charlottesville, VA 22904, United StatesEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilEscribo Leitura, Escribo - Educação do Seu Jeito, Estrada do Arraial, 2501, Recife, 52051-380, PE, BrazilPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Rua Benfica, 455, Recife, 50720-001, PE, BrazilThis study compares eleven widely used machine learning algorithms to identify the most accurate and comprehensive method for assessing children's reading fluency. Our fluency framework integrates three key dimensions: accuracy in word decoding, reading speed (words per minute), and prosody, which captures appropriate pausing and intonation during reading. Audio recordings from 2nd and 3rd grade students across 144 Brazilian schools were transcribed using the advanced Whisper ASR system, enabling automated extraction of fluency features. The research objective was to determine which algorithm best predicts fluency, considering diverse evaluation setups including binary classification (fluent versus non-fluent), multiclass classification (differentiated fluency levels), and regression analysis to estimate continuous fluency scores. Among the eleven models evaluated, Logistic Regression achieved the highest overall performance in the classification experiments, demonstrating superior precision and accuracy. Ensemble methods such as Gradient Boosting and Random Forest also yielded robust results, particularly in regression analyses where they effectively captured the variability in expert fluency ratings. Notably, reading speed emerged as the most critical indicator of fluency across all experiments, consistently outweighing contributions from accuracy and prosody. Nevertheless, prosody maintained significant importance, especially when fluency was modeled as a continuous variable, emphasizing the value of expressive reading. These findings suggest that while high accuracy in word decoding is fundamental, interventions that enhance reading speed and incorporate prosodic features can offer more nuanced support for reading development. This work provides educators with insights into scalable, automated, and cost-effective approaches to monitoring and improving reading fluency.http://www.sciencedirect.com/science/article/pii/S2666920X25000517Reading fluencyAutomatic speech recognitionLarge-scale assessmentNatural language processing |
| spellingShingle | Gabriel Candido da Silva Rodrigo Lins Rodrigues Américo N. Amorim Lieny Jeon Emilia X.S. Albuquerque Vanessa C. Silva Vinícius F. da Silva André L.A. Pinheiro João P.J.R. Nunes Suzana X.M.G. de Souza Maxsuel S. Silva Igor Mauro Alexandre Magno Andrade Maciel Assessing reading fluency in elementary grades: A machine learning approach Computers and Education: Artificial Intelligence Reading fluency Automatic speech recognition Large-scale assessment Natural language processing |
| title | Assessing reading fluency in elementary grades: A machine learning approach |
| title_full | Assessing reading fluency in elementary grades: A machine learning approach |
| title_fullStr | Assessing reading fluency in elementary grades: A machine learning approach |
| title_full_unstemmed | Assessing reading fluency in elementary grades: A machine learning approach |
| title_short | Assessing reading fluency in elementary grades: A machine learning approach |
| title_sort | assessing reading fluency in elementary grades a machine learning approach |
| topic | Reading fluency Automatic speech recognition Large-scale assessment Natural language processing |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X25000517 |
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