Efficacy of Implicit and Explicit Statistical Learning: A Randomized Controlled Trial Intervention for Chinese Children With Dyslexia

• In Event: Learning How the Brain Acquires Statistical Regularities: Implications for Education

Abstract

Objectives: An outstanding yet unresolved controversy is how reading should be taught to children, especially for children with dyslexia who struggle to read and write. Using a randomized controlled approach, this study examined the effects of implicit statistical training and explicit orthography-phonology training on word reading improvement in Chinese children with dyslexia.

Theoretical framework: The statistical-learning-based intervention for Chinese children with dyslexia was based on the Statistical Learning and Reading model, which assumes three core principles: (1) reading as statistical learning, (2) dynamic involvement of a multicomponent memory network, and (3) generality in statistical learning and heterogeneity in dyslexia.

Methods: Ninety-five Chinese children with dyslexia were randomly assigned to one of the following intervention groups: implicit, explicit, and no statistical learning of the orthography-to-phonology regularity in Chinese characters. Chinese word reading ability was measured before and after the intervention.

Materials: The statistical-learning-based intervention is defined as a program with ample and systematic presentation of the target structure. In this study, the target structure refers to the phonetic radical that predicts Chinese character pronunciation.

Results: The results showed that the implicit- or explicit-statistical-learning-based intervention outperformed the non-statistical-learning-based counterpart in teaching the quasi-regular orthography-to-phonology correspondence. This intervention study provides new insights into the debate on reading instruction methodology.

Significance: This study supports the SLR model that children with dyslexia rely on the statistical learning mechanism for cracking print codes.

Authors

• Stephen Man Kit Lee, University of Hong Kong
  Presenting Author

• Shelley Xiuli Tong, University of Hong Kong
  Non-Presenting Author