دورية أكاديمية
Early Prediction of Reading Risk in Fourth Grade: A Combined Latent Class Analysis and Classification Tree Approach
العنوان: | Early Prediction of Reading Risk in Fourth Grade: A Combined Latent Class Analysis and Classification Tree Approach |
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اللغة: | English |
المؤلفون: | Gutiérrez, Nuria (ORCID |
المصدر: | Scientific Studies of Reading. 2023 27(1):21-38. |
الإتاحة: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
Peer Reviewed: | Y |
Page Count: | 18 |
تاريخ النشر: | 2023 |
Sponsoring Agency: | Institute of Education Sciences (ED) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH) |
Contract Number: | R324G060036 P20HD091013 |
نوع الوثيقة: | Journal Articles Reports - Research |
Education Level: | Elementary Education Early Childhood Education Grade 1 Primary Education Grade 4 Intermediate Grades |
Descriptors: | Elementary School Students, Grade 1, Grade 4, Models, Prediction, Reading Difficulties, At Risk Students, Classification, Oral Language, Reading Skills, Early Intervention, Reading Comprehension, Accuracy, Word Recognition, Reading Tests |
Assessment and Survey Identifiers: | Gates MacGinitie Reading Tests |
DOI: | 10.1080/10888438.2022.2121655 |
تدمد: | 1088-8438 1532-799X |
مستخلص: | Purpose: Fourth grade typically involves shifting the instruction from "learning to read" to "reading to learn," which can cause students to struggle. However, early reading intervention guided by assessment has demonstrated effectiveness in preventing later reading difficulties (RD). This study presents a classification and regression tree (CART) model predicting fourth-grade reading groups using first-grade measures. Method: Students were assessed in first and fourth grade (N = 452). Fourth-grade groups were determined using latent class analysis based on word reading and reading comprehension measures with a cut-point at the 15th percentile. A CART model was trained to determine the best decision rules to classify students at risk of developing later RD and compared to a logistic regression model. Results: Important first-grade predictors included a mix of oral language and foundational word-reading skills with final classification accuracy estimates of 0.90 AUC, 0.91 sensitivity, and 0.75 specificity. Conclusion: While the CART and logistic regression models' classification accuracy was similar, CART has the advantage of offering a more intuitive way for practitioners to determine risk. Multivariate screening can be timeconsuming, but CART models offer the potential to reduce false positives and guide targeted interventions, leading to better use of school resources. |
Abstractor: | As Provided |
IES Funded: | Yes |
Entry Date: | 2023 |
رقم الأكسشن: | EJ1376187 |
قاعدة البيانات: | ERIC |
تدمد: | 1088-8438 1532-799X |
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DOI: | 10.1080/10888438.2022.2121655 |