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清华大学教育研究[ISSN:1001-4519/CN:11-1610/G4]

卷:

期数:

2023年06期

页码:

1-11

栏目:

OA栏目

出版日期:

2023-12-20

文章信息/Info

Title:

Intelligentization of Engineering Education: Connotation, Characteristics and Challenges

作者:

林 健 杨 冬

清华大学 教育研究院

Author(s):

LIN Jian YANG Dong

Institute of Education, Tsinghua University

关键词:

工程教育; 数字化; 智能化; 人工智能; 卓越工程师; 智慧教育

Keywords:

engineering education;  digitalization;  intelligentization;  artificial intelligence;  excellent engineers;  smart education

分类号:

G642.0

文献标志码:

A

摘要:

人工智能、大数据、虚拟现实等新兴技术正在重构高等教育形态,智能化是工程教育在“数字-智能时代”的变革趋势。本文在阐述工程教育智能化的必要性和紧迫性之后,提出工程教育智能化是依托新兴数字智能技术,变革和创新工程教育,构建面向未来“数字-智能时代”的工程教育新形态、工程人才培养新模式和工程教育教学新方式,并且将其内涵具体诠释为智能技术在工程教育中的有机内嵌和深度应用以及支撑并赋能工程教育的系统转型和体系重构。在此基础上,系统解析了工程教育智能化所具有的技术集群耦合、资源多元集成、平台一体智联、人机协同交互、时空开放泛在、教学精准高效、学习个性自适、实践虚实贯通、评价数据驱动等基本特征,客观分析了工程教育智能化面临的教育教学环境智能化升级、教师与学生角色定位转型、教学内容和教学方式革新、重构工程实践教学体系、打破教师与学生惯性惰性、优化教学效果和质量评价等诸多挑战,以期为工程教育智能化的相关研究及系统设计与实现提供参考和借鉴。

Abstract:

Emerging technologies such as artificial intelligence, big data, and virtual reality are reconstructing the form of higher education, and intelligentization is the reforming tendency of engineering education in the “digital-intelligent era”. After explaining the necessity and urgency of engineering education intelligentization, this paper proposes that engineering education intelligentization is to rely on emerging digital and intelligent technologies to reform and innovate engineering education, and to construct a new form of engineering education, a new model of engineering talent training and a new way of engineering education and teaching for the future “digital-intelligent era”. Moreover, its connotation is specifically interpreted as the organic embeddedness and in-deep application of intelligent technology in engineering education, as well as the system transformation and system reconstruction that support and empower engineering education. On this basis, this paper systematically analyzes the basic characteristics of intelligentization of engineering education, such as technology cluster coupling, multi-resource integration, platform integration and intelligent connection, human-computer collaborative interaction, open and ubiquitous time and space, accurate and efficient teaching, self-adaptation and personalized learning, virtual and real integration of practice, and evaluation data-driven. At last, this paper objectively analyzes the challenges faced by engineering education intelligentization, such as intelligent upgrading of the education and teaching environment, the transformation of teachers’ and students’ role positioning, the innovation of teaching content and teaching methods, the reconstruction of the engineering practice teaching system, breaking the inertia of teachers and students, and optimizing teaching effect and quality evaluation, in order to provide reference for the related research and systematic design and implementation of engineering education intelligentization.

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更新日期/Last Update: 2023-12-20