Identifying Determinants of Dyslexia: An Ultimate Attempt Using Machine Learning

Abstract

Research based on traditional linear techniques has yet not been able to clearly identify the role of cognitive skills in reading problems, presumably because the process of reading and the factors that are associated with reading reside within a system of multiple interacting and moderating factors that cannot be captured within traditional statistical models. If cognitive skills are indeed indicative of reading problems, the relatively new nonlinear techniques of machine learning should make better predictions. The aim of the present study was to investigate whether cognitive factors play any role in reading skill, questioning (1) the extent to what cognitive skills are indicative of present reading level, and (2) the extent to what cognitive skills are indicative of future reading progress. In three studies with varying groups of participants (average school-aged and poor readers), the results of four supervised machine learning techniques were compared to the traditional General Linear Models technique. Results of all models appeared to be comparable, producing poor to acceptable results, which are however inadequate for making a thorough prediction of reading development. Assumably, cognitive skills are not predictive of reading problems, although they do correlate with one another. This insight has consequences for scientific theories of reading development, as well as for the prevention and remediation of reading difficulties.

Keywords: cognitive skills; dyslexia; machine learning; reading development; word decoding and reading outcomes.

FIGURE 1

Schematic overview of conducted studies and aims, research questions, and model building techniques that were involved. RQ, research question.

FIGURE 2

ROC-curves for the predictive ability of the models in all three studies built with five machine learning techniques.