Identifying Reading Fluency in Pupils with and without Dyslexia Using a Machine Learning Model on Texts Assessed with a Readability Application

• Published in: CEPS journal 2023-12, Vol.13 (4), p.233-256
• Main Authors: Žabkar, Jure, Javornik, Karmen, Košak Babuder, Milena, Urankar, Tajda
• Format: Article
• Language: English
• Subjects: dyslexia; machine learning; readability application; reading fluency
• ISSN: 1855-9719; 2232-2647
• DOI: 10.26529/cepsj.1367

Measurement of readability is an important tool for assessing reading disorders such as dyslexia. Among the screening procedures for dyslexia is the reading fluency test, which is defined as the ability to read with speed, accuracy and proper expression. The reading fluency test often consists of a sequence of unrelated written texts ranging from simple short sentences to more difficult and longer paragraphs. In psychological testing instruments, subjective text assessment is often replaced by objective readability formulas, e.g., the Automated Readability Index. Readability formulas extract multiple features from a given text and output a score indicating the difficulty of the text. The aim of the present study is to build a machine learning model that discriminates between pupils identified with dyslexia and a control group without dyslexia based on fluency in oral reading of texts assessed with a readability application developed within the project For the Quality of Slovenian Textbooks. We focus on differentiation between both groups of pupils by analysing data obtained from transcriptions of audio recordings of oral reading. The empirical study was conducted with 27 pupils aged 8 and 9 with officially diagnosed dyslexia and a control group without identified dyslexia.