A teaching quality evaluation framework for blended classroom modes with multi-domain heterogeneous data integration

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-05-23 DOI:10.1016/j.eswa.2025.127884

Yu Zhou , Sai Zou , Minghui Liwang , Yanglong Sun , Wei Ni

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引用次数: 0

Abstract

The blended teaching model, combining online and traditional classroom education, has become a normalized instructional approach in the artificial intelligence (AI) era. However, effectively integrating diverse educational data, accurately evaluating student learning, and providing personalized teaching recommendations remain key challenges. To address these, this study develops an AI-driven framework for evaluating teaching quality in blended classrooms. The framework aggregates multi-source educational data from both physical and digital learning environments, enabling a comprehensive assessment of student progress. Based on the evaluation outcomes, it leverages AI to generate personalized teaching strategies aligned with each student’s learning profile. Applied on a large scale in Western China, this framework has demonstrated significant improvements in teaching quality and efficiency. By bridging educational theory and AI-driven applications, this study offers a scalable model for enhancing personalized learning experiences in global digital education.

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基于多领域异构数据集成的混合式课堂教学质量评价框架

将在线教育与传统课堂教育相结合的混合式教学模式，已成为人工智能时代的一种常态化教学方式。然而，如何有效地整合各种教育数据，准确评估学生的学习情况，并提供个性化的教学建议仍然是关键的挑战。为了解决这些问题，本研究开发了一个人工智能驱动的框架，用于评估混合式教室的教学质量。该框架汇集了来自物理和数字学习环境的多源教育数据，从而能够全面评估学生的进步。根据评估结果，它利用人工智能来生成与每个学生的学习概况相一致的个性化教学策略。该框架在中国西部地区的大规模应用，在教学质量和效率方面取得了显著的进步。通过将教育理论与人工智能驱动的应用相结合，本研究提供了一个可扩展的模型，用于增强全球数字教育中的个性化学习体验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.