Browsing by Author "Adomako, O.S."

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    Gamified Learning Applications for Children
    (UENR, 2025-09) Adomako, O.S.
    This research addresses the critical challenge of personalization in early childhood educational applications through the design, development, and empirical evaluation of QuestKids, a gamified learning application featuring a novel Dynamic Difficulty Adjustment (DDA) algorithm. While gamification has demonstrated potential for enhancing engagement in learning environments, most existing applications for young learners (ages 5-6) lack robust technical mechanisms for real-time, performance-driven adaptation, resulting in either frustration or boredom due to static content progression. The primary contribution of this work is the implementation and validation of a rulebased DDA algorithm that dynamically modulates question difficulty based on a rolling average of user performance, maintaining learners within an optimal "flow zone" of 65-85% success rate. Developed using the Flutter framework with a V-Model methodology, QuestKids integrates adaptive learning modules for mathematics and reading with a comprehensive gamification engine featuring badges, virtual currency, and progression systems. A comparative evaluation study was conducted with 85 children over six weeks, benchmarking QuestKids against two established educational applications (Duolingo Kids and Khan Academy Kids). Results demonstrated that QuestKids achieved significantly higher engagement metrics, with 56% longer average session duration and 78% daily return rate compared to benchmark applications. Learning outcomes showed a 30% higher knowledge gain and 85% retention rate after one week. Statistical analysis confirmed the superiority of the adaptive approach, with significant differences in both engagement (p < 0.01) and learning gains (F=6.34, p=0.002). The study concludes that a carefully engineered DDA algorithm, integrated within a gamified learning framework, can effectively enhance both engagement and educational outcomes in early childhood education. This research contributes to educational technology by providing a validated technical model for adaptive learning systems and offering practical insights for developers and educators seeking to create more personalized, effective digital learning tools.