Microgenetic Analysis of Reading Remediation : A Novel Computational Framework

Reading intervention program efficacy is usually determined by comparing participants’ performance to controls on dependent measures at pre-, mid-, and post-intervention assessments. However, little is known about how learning progresses during different stages of the intervention. This lack of knowledge can be attributed to the absence of appropriate computational frameworks to encode, analyze, and capture such dynamics. We propose a novel computational framework to capture learning process dynamics during the intervention by analyzing microgenetic data. The framework addresses the problem of encoding microgenetic data into a common data rep resentation model, introduces four information-theoretic metrics to capture the instantaneous developmental learning stages of groups and individuals, and provides the mathematical model to analyze those metrics for the study of learning stages during the intervention. We used data from a longitudinal reading remediation study involving 56 Greek-speaking 6-year-old children to demonstrate the framework’s utility. Results showed that the framework functions as a new tool to explore the modulation in learning stages during the intervention, better understand how reading occurs, and how reading disability may be adequately treated.