Response time has been used as an important predictor of student performance in various models. Much of this work is based on the hypothesis that if students respond to a problem step too quickly or too slowly, they are most likely to be unsuccessful in that step. However, something that is less explored is that students may cycle through…

While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using…

Text complexity has received increased attention due to the CCSS, which call for students to comprehend increasingly more complex texts as they progress through grades. Quantitative text complexity (or readability) indices are based on text attributes (e.g., sentence lengths, and lexical, syntactic, & semantic difficulty), quantified by…

Despite prevalent use and practical application, the current and standard assessment of oral reading fluency (ORF) presents considerable limitations which reduces its validity in estimating growth and monitoring student progress, including: (a) high cost of implementation; (b) tenuous passage equivalence; and (c) bias, large standard error, and…

Lack of reading fluency is a significant obstacle to children's ability to learn from text. In this study, we explored the effects of two practices used to scaffold fluency -- repeated reading of a passage and listening to a passage before reading it. Since the differences between these practices may be subtle, we employed eye tracking to measure…