An Advanced Modeling Approach to Examine Factors Affecting Preschool Children's Phonological and Print Awareness

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 (R[superscript 2] = 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.