This study investigates the processes of visual word recognition in language learners using an individualized diagnostics and training framework. A training procedure based on the Lexical Categorization Model (LCM) was implemented to improve lexical categorization abilities, thereby enhancing reading speed. Three studies demonstrated increased reading skills in most learners. Machine learning procedures were used to predict the benefit of the training, achieving a 43% increase in reading speed in the selected group compared to 23% in the unselected group. The success of the prediction model strongly depended on LCM-associated parameters, highlighting the importance of lexical categorization in efficient reading.
Publisher
npj Science of Learning
Published On
Apr 10, 2024
Authors
Benjamin Gagl, Klara Gregorová
Tags
visual word recognition
language learners
reading speed
Lexical Categorization Model
machine learning
diagnostics
training
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