Design and Development of Effective Ideology and Political Educational Systems using Boosted Trees Models

Ideology and political education play a critical role in shaping civic understanding, social responsibility, and national consciousness, especially among students and young adults. Traditional methods of evaluating political educational systems often rely on static surveys and subjective assessments, which may fail to capture the dynamic and multifactorial nature of political awareness and ideological leanings. To address this, the present study proposes a data-driven framework leveraging ensemble learning methods specifically, AdaBoost and Decision Tree classifiers to develop and enhance the effectiveness of political educational systems. A custom dataset was manually collected and constructed through structured surveys and academic interviews, capturing diverse features such as demographic details, academic background, media exposure, political engagement levels, and ideological preferences of individuals. By applying Boosted Trees models, the framework enables accurate classification and prediction of ideological trends and learning outcomes, while offering interpretable decision rules to inform policy design and targeted educational interventions. Experimental results demonstrate that the proposed model achieves an accuracy of 97.6%, a precision of 96.8%, recall of 97.2%, and F1-score of 96.9%, outperforming standalone machine learning models. This study underscores the value of ensemble learning in civic education analytics and offers a scalable foundation for ideologically informed, student-centered curriculum planning.