Abstract
This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN models were trained using the data for phonological awareness and print awareness of children. According to the findings, the created ANN model had an excellent fit to the actual data (R2 = 0.934 and 0.940). Furthermore, the ANN model results were tested with a traditional analysis technique, Pearson correlation analysis. The ANN models yielded similar results to the Pearson correlation analysis but with more detail as expected. The ANN models were run for user-generated synthetic datasets and the relationships between the dependent and independent variables were discussed using model results. Demographic variables, namely, children’s age, mother’s age, mother’s education, and family income were found to be not effective on children’s print and phonological awareness skills. On the other hand, home literacy environment-related variables were found to be very effective. In conclusion, this paper introduces a methodology for implementing ANN modeling in educational data. A novel and powerful approach is provided to assess and estimate essential components of early literacy skills. The study has important implications for advancing our understanding of potential benefits of employing an AI-based modeling techniques in the field of education. The utilization of machine learning methods in educational research, as presented in this paper, has the potential to fundamentally reshape our approaches in categorizing and analyzing educational data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data was taken from the previous study by the author. All data collected by the author and generated by the modeling program are also submitted as supplemental material.
References
Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727. https://doi.org/10.1016/S0731-7085(99)00272-1
Baroody, A. E., & Diamond, K. E. (2012). Links among home literacy environment, literacy interest, and emergent literacy skills in preschoolers at risk for reading difficulties. Topics in Early Childhood Special Education, 32(2), 78–87. https://doi.org/10.1177/0271121410392803
Brooks-Gunn, J., Han, W., & Waldfogel, J. (2002). Maternal employment and child cognitive outcomes in the first three years of life: The NICHD study of early child care. Child Development, 73(4), 1052–1072. https://doi.org/10.1111/1467-8624.00457
Buhs, E. S., Welch, G., Burt, J., & Knoche, L. (2011). Family engagement in literacy activities: Revised factor structure for The Familia an instrument examining family support for early literacy development. Early Child Development and Care, 181(7), 989–1006. https://doi.org/10.1080/03004430.2011.564758
Burchinal, M. R., Peisner Feinberg, E., Pianta, R., & Howes, C. (2002). Development of academic skills from preschool through second grade: Family and classroom predictors of developmental trajectories. Journal of School Psychology, 40(5), 415–436. https://doi.org/10.1016/S0022-4405(02)00107-3
Caravolas, M., Lervåg, A., Mousikou, P., Efrim, C., Litavský, M., Onochie-Quintanilla, E., ... & Hulme, C. (2012). Common patterns of prediction of literacy development in different alphabetic orthographies. Psychological Science, 23(6), 678–686. https://doi.org/10.1177/0956797611434536
Chowdhury, S., & Saha, P. D. (2013). Artificial neural network (ANN) modeling of adsorption of methylene blue by NaOH-modified rice husk in a fixed-bed column system. Environmental Science and Pollution Research, 20, 1050–1058. https://doi.org/10.1007/s11356-012-0912-2
Coşkun, M. İ, & Karahan, İH. (2018). Modeling corrosion performance of the hydroxyapatite coated CoCrMo biomaterial alloys. Journal of Alloys and Compounds, 745, 840–848. https://doi.org/10.1016/j.jallcom.2018.02.253
Coşkun, L. (2023). Examination of Home Early Literacy Environment and Reading Beliefs of Mothers with Preschoolers in Terms of Demographic Variables. The Journal of Turkish Educational Sciences, 21(1), 425–452. https://doi.org/10.37217/tebd.1189625
DeBaryshe, B. D. (1995). Maternal belief systems: Linchpins in the home reading process. Journal of Applied Developmental Psychology, 16(1), 1–20. https://doi.org/10.1016/0193-3973(95)90013-6
DeBaryshe, B. D., Binder, J. C., & Buell, M. J. (2000). Mother’s implicit theories of early literacy instruction: Implications for children’s reading and writing. Early Child Development and Care, 160(1), 119–131. https://doi.org/10.1080/0030443001600111
DesJardin, J. L., & Ambrose, S. E. (2010). The importance of the home literacy environment for developing literacy skills in young children who are deaf or hard of hearing. Young Exceptional Children, 13(5), 28–44. https://doi.org/10.1177/1096250610387270
Dynia, J. M., Purtell, K. M., Justice, L. M., Pratt, A. S., & Hijlkema, M. J. (2020). Home literacy environments in Maya communities in the Yucatan Peninsula. Early Education and Development, 31(3), 411–425. https://doi.org/10.1080/10409289.2019.1651813
Elias, G., Hay, I., Homel, R., & Freiberg, K. (2006). Enhancing parent-child book reading in a disadvantaged community. Australasian Journal of Early Childhood, 31(1), 20–25. https://doi.org/10.1177/183693910603100104
Evis, Z., & Arcaklioglu, E. (2011). Artificial neural network investigation of hardness and fracture toughness of hydroxylapatite. Ceramics International, 37(4), 1147–1152. https://doi.org/10.1016/j.ceramint.2010.10.037
Farley, K. S., & Piasta, S. B. (2020). Examining early childhood language and literacy learning opportunities in relation to maternal education and children’s initial skills. Journal of Education for Students Placed at Risk (JESPAR), 25(3), 183–200. https://doi.org/10.1080/10824669.2019.1689506
Foy, J. G., & Mann, V. (2003). Home literacy environment and phonological awareness in preschool children: Differential effects for rhyme and phoneme awareness. Applied Psycholinguistics, 24(1), 59–88. https://doi.org/10.1017/S0142716403000043
Frijters, J. C., Barron, R. W., & Brunello, M. (2000). Direct and mediated influences of home literacy and literacy interest on prereaders’ oral vocabulary and early written language skill. Journal of Educational Psychology, 92(3), 466–477. https://doi.org/10.1037/0022-0663.92.3.466
Fung, W. K., & Chung, K. K. H. (2020). The role of socioeconomic status in Chinese word reading and writing among Chinese kindergarten children. Reading and Writing, 33(2), 377–397. https://doi.org/10.1007/s11145-019-09967-2
Gerde, H. K., Bingham, G. E., & Wasik, B. A. (2012). Writing in early childhood classrooms: Guidance for best practices. Early Childhood Education Journal, 40, 351–359. https://doi.org/10.1007/s10643-012-0531-z
Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264. https://doi.org/10.1016/S0304-3800(02)00257-0
Gonzalez, J. E., & Uhing, B. (2008). Home literacy environments and young Hispanic children’ English and Spanish oral language. Journal of Early Intervention, 30(2), 116–139. https://doi.org/10.1177/1053815107313858
Hammer, C. S., Farkas, G., & Maczuga, S. (2010). The language and literacy development of head start children: A study using the family and child experiences survey database. Language, Speech, and Hearing Services in Schools, 41(1), 70–83. https://doi.org/10.1044/0161-1461(2009/08-0050)
Hartas, D. (2011). Families’ social backgrounds matter: Socio-economic factors, home learning and young children’s language, literacy and social outcomes. British Educational Research Journal, 37(6), 893–914. https://doi.org/10.1080/01411926.2010.506945
Hecht, S., & Close, L. (2002). Emergent literacy skills and training time uniquely predict variability in responses to phonemic awareness training in disadvantaged kindergartners. Journal of Experimental Child Psychology, 82(2), 93–115. https://doi.org/10.1016/s0022-0965(02)00001-2
Hiebert, E. H. (1981). Developmental patterns and interrelationships of preschool children’s print awareness. Reading Research Quarterly, 16(2), 236–260. https://doi.org/10.2307/747558
Hooper, S. R., Roberts, J. E., Nelson, L., Zeisel, S., & Kasambira Fannin, D. (2010). Preschool predictors of narrative writing skills in elementary school children. School Psychology Quarterly, 25(1), 1–12. https://doi.org/10.1037/a0018329
Horner, S. L. (2004). Observational learning during shared book reading: The effects on preschoolers’ attention to print and letter knowledge. Reading Psychology, 25(3), 167–188. https://doi.org/10.1080/02702710490484714
Justi, C. N. G., Henriques, F. G. & dos Reis Justi, F. R. (2021). The dimensionality of phonological awareness among Brazilian Portuguese-speaking children: a longitudinal study. Psicologia: Reflexão e Crítica, 34(26). https://doi.org/10.1186/s41155-021-00192-x
Justice, L. M., Weber, S. E., Ezell, H. K., and Bakeman, R. (2002). A sequential analysis of children’s responsiveness to parental print references during shared book-reading interactions. American Journal of Speech-Language Pathology, 1(1)1, 30–40. https://doi.org/10.1044/1058-0360(2002/004)
Justice, L. M., & Ezell, H. K. (2002). Use of storybook reading to increase print awareness in at-risk children. American Journal of Speech-Language Pathology, 11(1), 17–29. https://doi.org/10.1044/1058-0360(2002/003)
Karahan, İ. H., & Özdemir, R. (2010). A new modeling of electrical resistivity properties of ZnFe alloys using genetic programming. Optoelectronics and Advanced Materials – Rapid Communications, 4(6), 812–815.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714–717.
Korucu, I., Litkowski, E., & Schmitt, S. A. (2020). Examining associations between the home literacy environment, executive function, and school readiness. Early Education and Development, 31(3), 455–473. https://doi.org/10.1080/10409289.2020.1716287
Layes, S., Guendouz, M., Lalonde, R., & Rebai, M. (2022). Combined phonological awareness and print knowledge training improves reading accuracy and comprehension in children with reading disabilities. International Journal of Disability, Development and Education, 69(4), 1185–1199. https://doi.org/10.1080/1034912X.2020.1779914
Linver, M. R., Brooks Gunn, J., & Kohen, D. E. (2002). Family processes as pathways from income to young children’s development. Developmental Psychology, 38(5), 719–734. https://doi.org/10.1037/0012-1649.38.5.719
Liu, Y., Zhi, M., & Li, X. (2011). Parental age and characteristics of the offspring. Ageing Research Reviews, 10(1), 115–123. https://doi.org/10.1016/j.arr.2010.09.004
Liu, C., Georgiou, G. K., & Manolitsis, G. (2018). Modeling the relationships of parents’ expectations, family’s SES, and home literacy environment with emergent literacy skills and Word reading in Chinese. Early Childhood Research Quarterly, 43, 1–10. https://doi.org/10.1016/j.ecresq.2017.11.001
Liu, X., Lun, H., Fu, M., Fan, Y., Yi, L., Hu, W., & Zhuge, Q. (2020). AI-based modeling and monitoring techniques for future intelligent elastic optical networks. Applied Sciences, 10(1), 363.
Maki, H. S., Voeten, M. J. M., Vauras, M. M. S., & Poskiparta, E. H. (2001). Predicting writing skill development with word recognition and preschool readiness skills. Reading and Writing, 14, 643–672. https://doi.org/10.1023/A:1012071514719
Manolitsis, G., Georgiou, G. K., & Tziraki, N. (2013). Examining the effects of home literacy and numeracy environment on early reading and math acquisition. Early Childhood Research Quarterly, 28(4), 692–703. https://doi.org/10.1016/j.ecresq.2013.05.004
Marjanovik Umek, L., Podlesek, A., & Fekonja, U. (2005). Assessing the home literacy environment relationships to child language comprehension and expression. European Journal of Psychological Assessment, 21(4), 271–281. https://doi.org/10.1027/1015-5759.21.4.271
Martini, F. & Se´ne´chal, M. (2012). Learning literacy skills at home: Parent teaching, expectations, and child interest. Canadian Journal of Behavioral Science, 44(3), 210-221. https://doi.org/10.1037/a0026758
McDowell, K. D., Lonigan, C. J., & Howard, G. (2007). Relations among socioeconomic status, age, and predictors of phonological awareness. Journal of Speech, Language, and Hearing Research, 50(4), 1079–1092. https://doi.org/10.1044/1092-4388(2007/075)
McGinty, A. S., & Justice, L. M. (2009). Predictors of print knowledge in children with specific language impairment: Experiential and developmental factors. Journal of Speech, Language, and Hearing Research, 52, 81–97. https://doi.org/10.1044/1092-4388(2008/07-0279)
Melby-Lervåg, M., Lyster, S. A. H., & Hulme, C. (2012). Phonological skills and their role in learning to read: A meta-analytic review. Psychological Bulletin, 138(2), 322. https://doi.org/10.1037/a0026744
Myrtil, M. J., Justice, L. M., & Jiang, H. (2019). Home-literacy environment of low-income rural families: Association with child and caregiver level characteristics. Journal of Applied Developmental Psychology, 60, 1–10. https://doi.org/10.1016/j.appdev.2018.10.002
Okuyucu Akdaş, E. & Deniz, Ü. (2015). Anasınıfına Devam Eden Çocukların Kitap ile Birlikteliklerine ve Ailelerinin Özelliklerine Göre Okuma [A research on reading maturity with regard to association with book and families characteristics of the children who attend kindergarten]. Uluslararası Türk Eğitim Bilimleri Dergisi, 4, 15–27. https://dergipark.org.tr/en/pub/goputeb/issue/34518/381074
Orlando, L. V. (2005). Learning literacy though play using interactive texts during storybook reading: A parent/child experience. Journal of Early Childhood Teacher Education, 25(3), 247–253. https://doi.org/10.1080/1090102050250308
Pfost, M., Blatter, K., Artelt, C., Stanat, P., & Schneider, W. (2019). Effects of training phonological awareness on children’s reading skills. Journal of Applied Developmental Psychology, 65, 101067. https://doi.org/10.1016/j.appdev.2019.101067
Piasta, S. B., Justice, L. M., McGinty, A. S., & Kaderavek, J. N. (2012). Increasing young children’s contact with print during shared reading: Longitudinal effects on literacy achievement. Child Development, 83(3), 810–882. https://doi.org/10.1111/j.1467-8624.2012.01754.x
Plumb, A. P., Rowe, R. C., York, P., & Brown, M. (2005). Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithm. European Journal of Pharmaceutical Sciences, 25(4–5), 395–405. https://doi.org/10.1016/j.ejps.2005.04.010
Puglisi, M. L., Hulme, C., Hamilton, L. G., & Snowling, M. J. (2017). The home literacy environment is a correlate, but perhaps not a cause, of variations in children’s language and literacy development. Scientific Studies of Reading, 21(6), 498–514. https://doi.org/10.1080/10888438.2017.1346660
Puranik, C. S., Lonigan, J. C., & Kim, Y. (2011). Contributions of emergent literacy skills to name writing, letter writing, and spelling in preschool children. Early Childhood Research Quarterly, 26(4), 465–474. https://doi.org/10.1016/j.ecresq.2011.03.002
Sağlam, C., & Özyürek, A. (2022). The examining of the working memory and early literacy skills in preschoolers. Journal of Early Childhood Studies, 6(1), 82–101. https://doi.org/10.24130/eccdjecs.1967202261379.
Sarker, I. H. (2022). Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021). Mobile data science and intelligent apps: Concepts, AI-based modeling and research directions. Mobile Networks and Applications, 26, 285–303. https://doi.org/10.1007/s11036-020-01650-z
Sawyer, B. E., Cycyk, L. M., Sandilos, L. E., & Hammer, C. S. (2018). So many books they don’t even all fit on the bookshelf: An examination of low-income mothers’ home literacy practices, beliefs and influencing factors. Journal of Early Childhood Literacy, 18(3), 338–372. https://doi.org/10.1177/1468798416667542
Sénéchal, M. (2006). Testing the Home Literacy Model: Parent involvement in kindergarten is differentially related to grade 4 reading comprehension, fluency, spelling, and reading for pleasure. Scientific Studies of Reading, 10(1), 59–87. https://doi.org/10.1207/s1532799xssr1001_4
Sénéchal, M., & LeFevre, J. A. (2002). Parental involvement in the development of children’s reading skill: A five-year longitudinal study. Child Development, 73(2), 445–460. https://doi.org/10.1111/1467-8624.00417
Serpell, R., Sonnenschein, S., Baker, L., & Ganapathy, H. (2002). Intimate culture of families in the early socialization of literacy. Journal of Family Psychology, 16(4), 391–405. https://doi.org/10.1037/0893-3200.16.4.391
Shatil, E., Share, D. L., & Levin, I. (2000). On the contribution of kindergarten writing to grade 1 literacy: A longitudinal study in Hebrew. Applied Psycholinguistics, 21(1), 1–2. https://doi.org/10.1017/S0142716400001016
Sidney Smith, S., & Dixon, G. R. (1995). Literacy concepts of low and middle class four year olds entering preschool. Journal of Educational Research, 88(4), 243–253. https://doi.org/10.1080/00220671.1995.9941305
Silinskas, G., Leppa¨nen, U. Aunola, K., Parrila, R. & Nurmi, J. E. (2010). Predictors of mothers’ and fathers’ teaching of reading and mathematics during kindergarten and Grade. Learning and Instruction, 20(1), 61-71.https://doi.org/10.1016/j.learninstruc.2009.01.002
Storch, S. A., & Whitehurst, G. J. (2002). Oral language and code-related precursors to reading: Evidence from a longitudinal structural model. Developmental Psychology, 38(6), 934. https://doi.org/10.1037/0012-1649.38.6.934
Surya, S., Gupta, S., Mehbodniya, A., Panduro-Ramirez, J., Kapula, P. R., Alam, T., & Kaliyaperumal, K. (2022). Addressing the real world problem of managing wireless communication systems using explainable AI-based models through correlation analysis. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/3390075
Taylor, J. J. (2011). Paternal support of emergent literacy development: Latino fathers and their children. The International Honor Society in Psychology, 16(2), 58–72. https://doi.org/10.24839/1089-4136.JN16.2.58
Weigel, D. J., Martin, S. S., & Bennett, K. K. (2006). Mothers’ literacy beliefs: Connections with the home literacy environment and pre-school children’s literacy development. Journal of Early Childhood Literacy, 6(2), 191–211. https://doi.org/10.1177/1468798406066444
Welsch, J., Sullivan, A., & Justice, L. (2003). That’s my letter! What preschoolers’ name writing representations tell us about emergent literacy knowledge. Journal of Literacy Research, 35(2), 757–776. https://doi.org/10.1207/s15548430jlr3502_4
Wu, W., Dandy, G. C., & Maier, H. R. (2014). Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environmental Modelling & Software, 54, 108–127. https://doi.org/10.1016/j.envsoft.2013.12.016
Xie, X., Wang, L., & Wang, A. (2010). Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist, 80(2), 262–266. https://doi.org/10.2319/111608-588.1
Zhang, S. Z., Inoue, T., Shu, H., & Georgiou, G. K. (2020). How does home literacy environment influence reading comprehension in Chinese? Evidence from a 3 year longitudinal study. Reading and Writing, 33, 1745–1767. https://doi.org/10.1007/s11145-019-09991-2
Acknowledgements
I would like to thank M.İbrahim Coşkun (PhD, Information Technology) and Rasim Özdemir (PhD, Physics) for their help and support in building Artificial Network Architecture and preparing the data for modeling procedures.
Funding
The author received no financial support for the research.
Author information
Authors and Affiliations
Contributions
All study was carried out by the single author. Some external support received from other colleagues is explained in Acknowledgement.
Corresponding author
Ethics declarations
The data used in this study was taken from the previous study by the same author, titled "Examination of Home Literacy Environment and Reading Beliefs of Mothers with Preschoolers in Terms of Demographic Variables" (Coskun, 2023). All data collection procedures including Informed Consent, Ethical Approval, and Statement Regarding Research Involving Human Participants were explained in that study. The current study focuses only on employing an advanced modeling/statistical tool for the data and discussion of the modeling results.
Ethical approval
An Ethical Approval dated 07.04.2021 and numbered 2021/09 was received from Kilis 7 Aralık University Ethics Committee. Details are provided in Examination of Home Literacy Environment and Reading Beliefs of Mothers with Preschoolers in Terms of Demographic Variables.
Informed consent
Informed consent was received from the mothers of the children. Details are provided in Examination of Home Literacy Environment and Reading Beliefs of Mothers with Preschoolers in Terms of Demographic Variables.
Research involving human participants and/or animals
The study has been approved by the appropriate Kilis 7 Aralık University Ethics Committee and have been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Details are provided in Examination of Home Literacy Environment and Reading Beliefs of Mothers with Preschoolers in Terms of Demographic Variables.
Competing interests
The author has no competing interests to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Coşkun, L. An advanced modeling approach to examine factors affecting preschool children’s phonological and print awareness. Educ Inf Technol (2023). https://doi.org/10.1007/s10639-023-12216-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10639-023-12216-3