Title: Research on intelligent data collection and quality evaluation of computer science and art education systems based on systemic multi-information fusion approach

Authors: Wang Ziming

Addresses: Academy of Fine Arts, Anshan Normal University, Anshan, Liaoning, China

Abstract: This paper presents an intelligent system framework tailored to systematically evaluate student learning outcomes and teaching quality in computer science education using a multi-methodological approach. By leveraging multi-information fusion technology in conjunction with advanced data collection techniques, the framework integrates tools such as the Internet of Things (IoT), big data analytics and machine learning to enhance the accuracy and precision of data acquisition. Addressing the specific demands of computer science education, this research proposes a comprehensive, multi-dimensional evaluation model designed to holistically assess both student learning effectiveness and instructional performance. The study seeks to provide structured, objective methodologies to evaluate and improve the quality of computer science education through in-depth systemic analysis.

Keywords: multi-information fusion technology; systemic data collection; big data analytics; systemic teaching evaluation; computer science education systems.

DOI: 10.1504/IJCAT.2026.151385

International Journal of Computer Applications in Technology, 2026 Vol.78 No.1, pp.63 - 71

Received: 22 Nov 2024
Accepted: 27 Feb 2025
Published online: 26 Jan 2026 *

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