ABSTRACT

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 .90 AUC, .91 sensitivity, and .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 time-consuming, but CART models offer the potential to reduce false positives and guide targeted interventions, leading to better use of school resources.

中文翻译：

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四年级阅读风险的早期预测：联合潜在类分析和分类树方法

摘要

目的

四年级通常涉及将教学从学习阅读转变为阅读学习，这可能会导致学生挣扎。然而，以评估为指导的早期阅读干预已证明在预防后期阅读困难 (RD) 方面是有效的。本研究提出了一个分类和回归树 (CART) 模型，该模型使用一年级测量值预测四年级阅读群体。

方法

对一年级和四年级的学生进行评估（N = 452）。四年级组使用基于单词阅读和阅读理解测量的潜在类别分析确定，切点为第 15 个百分位数。训练 CART 模型以确定最佳决策规则，以对有发展为后期 RD 风险的学生进行分类，并与逻辑回归模型进行比较。

结果

重要的一级预测指标包括口语和基本单词阅读技能的组合，最终分类准确度估计为 0.90 AUC、0.91 灵敏度和 0.75 特异性。

结论

虽然 CART 和逻辑回归模型的分类准确度相似，但 CART 的优势在于为从业者提供了一种更直观的风险确定方式。多变量筛选可能很耗时，但 CART 模型提供了减少误报和指导有针对性的干预措施的潜力，从而更好地利用学校资源。