МОДУЛЬНА АРХІТЕКТУРА ВЕБЗАСТОСУНКУ ДЛЯ КОГНІТИВНОГО ТЕСТУВАННЯ З ЕЛЕМЕНТАМИ СТАТИСТИЧНОГО АНАЛІЗУ
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| Title: | МОДУЛЬНА АРХІТЕКТУРА ВЕБЗАСТОСУНКУ ДЛЯ КОГНІТИВНОГО ТЕСТУВАННЯ З ЕЛЕМЕНТАМИ СТАТИСТИЧНОГО АНАЛІЗУ |
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| Alternate Title: | MODULAR ARCHITECTURE OF A WEB APPLICATION FOR COGNITIVE TESTING WITH ELEMENTS OF STATISTICAL ANALYSIS. |
| Authors: | Комарський, І. О.1 illia.komarskyi@gmail.com, Бабич, Ю. І.1 babich.u.i@op.edu.ua, Бабич, М. І.1 babich.tiger@gmail.com, Літвінов, В. Ф.1 litvinov.v.f@op.edu.ua |
| Source: | Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì. 2025, Vol. 15 Issue 4, p567-575. 9p. |
| Subject Terms: | *COGNITIVE testing, *STATISTICS, *USER experience, *WEB-based user interfaces, *JAVASCRIPT programming language, *INFORMATION networks, *STATISTICAL accuracy, *MODULAR design |
| Abstract: | Modern web applications for cognitive testing are complex systems that operate under dynamic user interactions, variable loads, and strict data accuracy requirements, significantly complicating the processes of development, analysis, and optimization. Traditional implementation methods often neglect modularity, resulting in difficulties in scaling and integrating new functionalities, such as statistical analysis of results. This article proposes an integrated approach for creating a modular web application inspired by the Human Benchmark platform, based on JavaScript/TypeScript for logic, Tailwind CSS for the interface, and Firebase with Auth0 for storage and authentication. At its core, the system employs a unified component structure, including independent test modules (Reaction.tsx, Sequence.tsx, Aim.tsx), analysis algorithms (calculateImprovement.ts, getUsersRating.ts) with probabilistic models (linear regression, improvement calculations), and simulation-based results. Each test structure includes a description of the type, result evaluation, execution conditions, data sources, statistical methods, and interrelationships among elements. The system incorporates custom reverse-engineered tests, enhancing the accuracy of cognitive function diagnostics. To improve efficiency, data normalization, formula optimization (L-BFGS-B-like methods), and continuous addition of new scenarios are applied. Simulation modeling allows for accounting of both standard and rare deviations, providing a complete picture of possible outcomes. Results of numerical experiments confirm the effectiveness of the approach: correlation with norms reaches 0.85, error decreases by 20%, and the system demonstrates flexibility under changing conditions. The integration of modularity with statistical methods enables the prediction of improvements at different testing stages, reducing error risks and optimizing the user experience. The proposed system is an effective tool for cognitive assessment in computer science and can be adapted for other technically complex platforms. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
Nájsť tento článok vo Web of Science | Abstract: | Modern web applications for cognitive testing are complex systems that operate under dynamic user interactions, variable loads, and strict data accuracy requirements, significantly complicating the processes of development, analysis, and optimization. Traditional implementation methods often neglect modularity, resulting in difficulties in scaling and integrating new functionalities, such as statistical analysis of results. This article proposes an integrated approach for creating a modular web application inspired by the Human Benchmark platform, based on JavaScript/TypeScript for logic, Tailwind CSS for the interface, and Firebase with Auth0 for storage and authentication. At its core, the system employs a unified component structure, including independent test modules (Reaction.tsx, Sequence.tsx, Aim.tsx), analysis algorithms (calculateImprovement.ts, getUsersRating.ts) with probabilistic models (linear regression, improvement calculations), and simulation-based results. Each test structure includes a description of the type, result evaluation, execution conditions, data sources, statistical methods, and interrelationships among elements. The system incorporates custom reverse-engineered tests, enhancing the accuracy of cognitive function diagnostics. To improve efficiency, data normalization, formula optimization (L-BFGS-B-like methods), and continuous addition of new scenarios are applied. Simulation modeling allows for accounting of both standard and rare deviations, providing a complete picture of possible outcomes. Results of numerical experiments confirm the effectiveness of the approach: correlation with norms reaches 0.85, error decreases by 20%, and the system demonstrates flexibility under changing conditions. The integration of modularity with statistical methods enables the prediction of improvements at different testing stages, reducing error risks and optimizing the user experience. The proposed system is an effective tool for cognitive assessment in computer science and can be adapted for other technically complex platforms. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 22235744 |
| DOI: | 10.15276/imms.v15.no4.567 |