职业教育课堂教学变革:基于多模态数据驱动的深度学习研究

The Reform of Classroom Teaching in Vocational Education:A Study of Deep Learning Supported by Multimodal Data

随着人工智能等新兴技术的不断涌现与广泛应用,未来职业教育的课堂教学已全面转向培养高素质的创新型应用人才,旨在培养学生的高阶思维和创新实践能力的深度学习,正逐渐成为促进职业教育课堂教学变革的新理念、新方式。为此,从教学目标、教学策略、教学过程、教学评价四个环节构建了面向深度学习的教学设计框架,并基于伴随性评价理念和多模态数据技术,设计了多维度评估学生深度学习情况的数据采集指标,可支持多模态数据驱动的深度学习实现,但仍需要在未来不断加强智能空间的构建、提升教师的数字胜任力与数据素养、促进课堂教学与实训实操对接、确保多模态数据的准确和安全。

With the continuous emergence and wide application of emerging technologies such as artificial intelligence,the classroom teaching of Vocational Education in the future has turned to the cultivation of highquality innovative applied talents,aiming at cultivating students'high-level thinking and innovative practical ability.It is gradually becoming a new concept and new way to promote the classroom teaching reform of vocational education.Therefore,the teaching design framework for deep learning is constructed from four aspects of teaching objectives,teaching strategies,teaching process and teaching evaluation.Based on the concept of accompanying assessment and multimodal data technology,a data acquisition index for multi-dimensional evaluation of students'deep learning is designed,which can support the realization of multi-modal data-driven deep learning.In the future,we still need to strengthen the construction of intelligent space,improve teachers'digital competence and data literacy,and promote the docking of classroom teaching and practical training,so as to ensure the accuracy and safety of multimodal data.