Screens and Preschools: The Bilingual English Language Learner Assessment as a Curriculum-Compliant Digital Application - Hechmi Kilani; Ilia V. Markov; David Francis; Elena L. Grigorenko (MDPI AG, 2024)
Background/Objectives: The increase in digital tools in early childhood education highlights the need for evidence-based assessments that support cognitive development and align with educational requirements and technological advances. This study contributes to the evaluation of the Bilingual English Language Learner Assessment (BELLA), designed to enhance early learning through curriculum-aligned tasks in preschool-aged children. Methods: Data were collected from 17 schools, including 506 preschool children, using a mixed-model approach to assess BELLA's capacity to appraise early numeracy, literacy, science, and social/emotional development. Analyses included a three-way ANOVA to examine the effects of sex, age, and sub-domain on pass rates and mixed-effects models to evaluate interactions between age and domain. Results: The results indicated a significant effect of age on performance across all domains, with older children demonstrating higher pass rates (p < 0.0001). No significant gender bias was detected. The interaction between age and domain was also significant (p < 0.0001), suggesting domain-specific age-related performance trends, which aligns with internal validity requirements. Conclusion: These findings position BELLA within the growing body of literature on digital media use in early childhood assessment and education, highlighting its potential as a curriculum-compliant digital assessment tool that evaluates and supports cognitive development without a gender bias. This study contributes to the field by providing empirical evidence of BELLA's effectiveness and suggesting future research directions, including the exploration of its bilingual (and potentially multilingual) applications and external validation against existing evidence-based assessments.