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    Can Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performance
    (Routledge Journals, Taylor & Francis Ltd, 2024) Yildiz, Mustafa; Keskin, Hasan Kagan; Oyucu, Saadin; Hartman, Douglas K.; Temur, Murat; Aydogmus, Mucahit
    This study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students' reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in T & uuml;rkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial intelligence-based automatic speech recognition system analyzed the recordings for reading errors. Following the analysis, a word error rate was calculated for both the experts and the artificial intelligence-based automatic speech recognition system. Word error rates were converted into reading accuracy rate scores. Inter-rater agreement and linear regression analyses were used to compare the raters' reading fluency scores, and logistic regression analyses were used to compare the classification of readers according to their reading levels. Results showed that the difference between the scores of the artificial intelligence-based automatic speech recognition system and the expert scores was minimal. This is because there was a very high level of agreement between the artificial intelligence-based automatic speech recognition system and the experts scores. Linear regression analyses showed that the artificial intelligence-based automatic speech recognition system significantly predicted the scores of experts. According to the logistic regression analysis results, the artificial intelligence-based automatic speech recognition system was at least 93% as successful as human raters in classifying readers as poor and good. These results give us hope that reading assessments at classroom, school, regional, national, and even international levels can be conducted more accurately and economically by using artificial intelligence-based systems in the coming years.
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    Examining the Interaction Between Parents' Education Level and Children's Books and its Impact on Early Literacy Skills
    (Fac Teacher Education, 2023) Keskin, Hasan Kagan; Yazici, Elcin
    In this study, it is aimed to investigate the individual and interactive impact of the number of children's books at home and the educational background of parents on the Skill of Phonological Awareness and Alphabet Knowledge, which have a critical influence on the reading skills of children in the preschool period. This study examines the variables Family and Reading Skills in a sample composed of 149 children and their parents in a locality in the capital of Turkey. The Literacy Skills Survey Test (LSST) to assess Phonological Awareness and Alphabet Knowledge skills and the general information form to establish the number of children's books and parents' education levels were employed. The between-group differences and interactions were analyzed with multivariate analysis of variance (MANOVA). The analysis of the number of books and education level revealed a significant difference only in Phonological Awareness. No interaction effect on alphabet knowledge was identified. However, the main effects of the Number of books and Education level variables on Alphabet Knowledge were significant. The group of parents with a university degree with a small number of books accounted for the difference in Phonological Awareness and had the highest Phonological Awareness points. The combination of the Parents ' education level and the Number of children's books at home variables can affect Phonological Awareness. These variables also have personal effects on Alphabet Knowledge. However, what renders the number of books more effective in Phonological Awareness is the parents' education level.