工业技术与职业教育

随着生成式人工智能技术的兴起，充分展现了机器深度学习带来的无限潜能。对于学习者深度学习的研究目前主要集中在深度学习方式、策略和评价三个方面，未从可评、可测、可量的角度揭示学习者深度学习是否发生以及关联影响因素。结合高等职业院校学生学习特点，采用混合分析法，以机器深度学习的视角，基于班杜拉三元交互理论在宏观层面构建分析框架，从个人、行为和环境三因素方面深入挖掘影响学生深度学习的关联因素；从微观层面深入职业教育教学一线，利用实际数据和机器学习算法进行关联影响因素的实证分析。结果显示课堂表现较好的学生深度学习特征明显；得分越高的学生，深度学习的可能性越大；良好的学习环境有助于学生深度学习；学生的课程总评成绩与是否深度学习并不完全呈正相关。本研究为促进深度学习在教育教学中的有效应用提供了参考。

With the rise of generative AI technology,the unlimited potential brought by machine deep learning is fully demonstrated.Currently,research on deep learning for learners have mainly focused on the three aspects of deep learning mode,strategy and evaluation,and have not revealed whether learners' deep learning occurs and the associated influencing factors from the perspective of assessable,measurable and quantifiable.This paper combines the learning characteristics of students in higher vocational colleges,adopts the hybrid analysis method,with the perspective of machine deep learning,builds an analytical framework at the macro level based on Bandura's ternary interaction theory,digs deep into the correlated factors affecting students' deep learning in terms of personal,behavioral,and environmental factors,and penetrate into the frontline of vocational education teaching from the micro level,using actual data and machine learning algorithms to carry out an empirical analysis of the correlated influencing factors.empirical analysis.The results show that students with better classroom performance have obvious characteristics of deep learning;the higher the score,the higher the possibility of deep learning;a good learning environment helps students to learn deeply;and students' overall course evaluation grades are not completely positively correlated with whether or not they learn deeply.This study provides a reference for promoting the effective application of deep learning in education and teaching.