Reading Difficulties Identification: A Comparison of Neural Networks, Linear, and Mixture Models

Autor/inn/en	Psyridou, Maria; Tolvanen, Asko; Patel, Priyanka; Khanolainen, Daria; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Torppa, Minna
Titel	Reading Difficulties Identification: A Comparison of Neural Networks, Linear, and Mixture Models
Quelle	In: Scientific Studies of Reading, 27 (2023) 1, S.39-66 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Psyridou, Maria) ORCID (Tolvanen, Asko) ORCID (Khanolainen, Daria) ORCID (Lerkkanen, Marja-Kristiina) ORCID (Poikkeus, Anna-Maija) ORCID (Torppa, Minna)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1088-8438
DOI	10.1080/10888438.2022.2095281
Schlagwörter	Reading Difficulties; Networks; Artificial Intelligence; Predictor Variables; Kindergarten; Gender Differences; Phonological Awareness; Reading Comprehension; Reading Fluency; Accuracy; Models; Mothers; Educational Attainment; Naming; Reading Research; Family Literacy; Grade 9; Foreign Countries; Longitudinal Studies; Identification; Student Characteristics; Finland + Suchen Sie Ihr Suchwort? Reading difficulty; Leseschwierigkeit; Künstliche Intelligenz; Prädiktor; Geschlechterkonflikt; Leseverstehen; Analogiemodell; Mother; Mutter; Bildungsabschluss; Bildungsgut; Leseforschung; School year 09; 9. Schuljahr; Schuljahr 09; Ausland; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Identifikation; Identifizierung; Finnland
Abstract	Purpose: We aim to identify the most accurate model for predicting adolescent (Grade 9) reading difficulties (RD) in reading fluency and reading comprehension using 17 kindergarten-age variables. Three models (neural networks, linear, and mixture) were compared based on their accuracy in predicting RD. We also examined whether the same or a different set of kindergarten-age factors emerge as the strongest predictors of reading fluency and comprehension difficulties across the models. Method: RD were identified in a Finnish sample (N [approximately equal to] 2,000) based on Grade 9 difficulties in reading fluency and reading comprehension. The predictors assessed in kindergarten included gender, parental factors (e.g., parental RD, education level), cognitive skills (e.g., phonological awareness, RAN), home literacy environment, and task-avoidant behavior. Results: The results suggested that the neural networks model is the most accurate method, as compared to the linear and mixture models or their combination, for the early prediction of adolescent reading fluency and reading comprehension difficulties. The three models elicited rather similar results regarding the predictors, highlighting the importance of RAN, letter knowledge, vocabulary, reading words, number counting, gender, and maternal education. Conclusion: The results suggest that neural networks have strong promise in the field of reading research for the early identification of RD. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01