智慧学习环境下高职课堂教学互动效果影响因素研究

Research on Influencing Factors of Classroom Teaching Interaction in Higher Vocational Colleges under Smart Learning Environment

摘要: 随着人工智能、大数据、物联网等新一代信息技术与高职教育的深度融合，智慧学习环境已成为推动职业教育数字化转型的重要载体，但教学互动效果不佳的问题依然突出。本研究以穆尔交互影响距离理论和协同教学理论为基础，将高职课堂中的教学互动分为师生、生生、师师以及资源四个维度，从教师特质、学生特质与环境维度三方面提取关键变量，进而采用文献分析法、深度访谈与问卷测量相结合的混合研究路径，系统探析智慧学习情境下教学互动效果的驱动因素。研究选取安徽省三所高职院校的8名教师进行访谈，286名学生参与问卷调查，经过德尔菲专家咨询和数据分析确定智慧学习环境下高职课堂教学互动效果影响因素。结果表明：教师特质、教学设计、信息技术应用能力、学习态度、社会临场感、技术支撑、智慧空间布局、学习氛围八个因素对教学互动具有显著正向影响，同伴压力和自主学习能力影响不显著。据此提出构建智能协同机制、优化教学互动策略、升级智慧学习环境的对策建议。

Abstract: With the deep integration of new-generation information technologies such as artificial intelligence, big data, and the Internet of Things into vocational education, smart learning environments have become an important driver for the digital transformation of vocational education, but the problem of poor teaching interaction remains prominent. This study, based on Moore’s theory of transactional distance and collaborative teaching theory, divides teaching interaction in vocational classrooms into four dimensions: teacher-student, student-student, teacher-teacher, and resources. It also extracts key variables from teacher traits, student traits, and the environmental dimension. Using a mixed research approach combining literature analysis, in-depth interviews, and questionnaire surveys, the study systematically explores the driving factors of teaching interaction effects in smart learning contexts. Eight teachers from three vocational colleges in Anhui Province were interviewed, and 286 students participated in the questionnaire survey. Through Delphi expert consultation and data analysis, the factors affecting teaching interaction in vocational classrooms under smart learning environments were identified. The results show that eight factors—teacher characteristics, instructional design, information technology application ability, learning attitude, social presence, technical support, smart space layout, and learning atmosphere—have a significant positive impact on teaching interaction, while peer pressure and self-directed learning ability are not significant. Based on this, suggestions are made to build intelligent collaboration mechanisms, optimize teaching interaction strategies, and upgrade smart learning environments.