A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Reading Difficulties Identification : A Comparison of Neural Networks, Linear, and Mixture Models (2023)


Psyridou, M., Tolvanen, A., Patel, P., Khanolainen, D., Lerkkanen, M.-K., Poikkeus, A.-M., & Torppa, M. (2023). Reading Difficulties Identification : A Comparison of Neural Networks, Linear, and Mixture Models. Scientific Studies of Reading, 27(1), 39-66. https://doi.org/10.1080/10888438.2022.2095281


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Psyridou, Maria; Tolvanen, Asko; Patel, Priyanka; Khanolainen, Daria; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Torppa, Minna

Lehti tai sarja: Scientific Studies of Reading

ISSN: 1088-8438

eISSN: 1532-799X

Julkaisuvuosi: 2023

Ilmestymispäivä: 18.07.2022

Volyymi: 27

Lehden numero: 1

Artikkelin sivunumerot: 39-66

Kustantaja: Taylor & Francis

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1080/10888438.2022.2095281

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/82553


Tiivistelmä

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 ≈ 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.


YSO-asiasanat: lukutaito; lukeminen; luetun ymmärtäminen; oppimisvaikeudet; lukihäiriöt; tunnistaminen; ennustettavuus; mallit (mallintaminen); kognitiiviset taidot; hermoverkot (biologia)


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2023

JUFO-taso: 3


Viimeisin päivitys 2024-12-10 klo 15:30