Exploring the Machine Learning Paradigm in Determining Risk for Reading Disability

Autor/inn/en	Erbeli, Florina; He, Kai; Cheek, Connor; Rice, Marianne; Qian, Xiaoning
Titel	Exploring the Machine Learning Paradigm in Determining Risk for Reading Disability
Quelle	In: Scientific Studies of Reading, 27 (2023) 1, S.5-20 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Erbeli, Florina) ORCID (Rice, Marianne) ORCID (Qian, Xiaoning)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1088-8438
DOI	10.1080/10888438.2022.2115914
Schlagwörter	At Risk Students; Reading Difficulties; Classification; Comparative Analysis; Reading Fluency; Grade 1; Grade 2; Elementary School Students; Accuracy; Predictor Variables; Identification; Artificial Intelligence; Dyslexia; Emergent Literacy; Reading Tests; Reading Programs; Grade 3; Achievement Tests; Standardized Tests; State Standards; Reading Comprehension; Reading Research; Correlation; Statistical Analysis; Intelligence Tests; Vocabulary; Verbal Ability; Florida; Dynamic Indicators of Basic Early Literacy Skills (DIBELS); Florida Comprehensive Assessment Test; Peabody Picture Vocabulary Test + Suchen Sie Ihr Suchwort? Reading difficulty; Leseschwierigkeit; Classification system; Klassifikation; Klassifikationssystem; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Prädiktor; Identifikation; Identifizierung; Künstliche Intelligenz; Dyslexics; Legasthenie; Lese-Rechtschreib-Schwäche; Frühleseunterricht; Lesetest; School year 03; 3. Schuljahr; Schuljahr 03; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Standadised tests; Standardisierter Test; Leseverstehen; Leseforschung; Korrelation; Statistische Analyse; Intelligence test; Intelligenztest; Wortschatz; Mündliche Leistung
Abstract	Purpose: Researchers have developed a constellation model of decodingrelated reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation model framework. However, some issues may arise with applying the logistic regression method (e.g., multicollinearity). Machine learning techniques, such as random forest (RF), might assist in overcoming these limitations. They can better deal with complex data relations than traditional approaches. We examined the prediction performance of RF and compared it against LR to determine RD risk. Method: The sample comprised 12,171 students from Florida whose thirdgrade RD risk was operationalized using the constellation model with one, two, three, or four RD indicators in first and second grade. Results: Results revealed that LR and RF performed on par in accurately predicting RD risk. Regarding predictor importance, reading fluency was consistently the most critical predictor for RD risk. Conclusion: Findings suggest that RF does not outperform LR in RD prediction accuracy in models with multiple linearly related predictors. Findings also highlight including reading fluency in early identification batteries for later RD determination. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01