Construction and Empirical Research on the Interactive Influencing Factors Model of Programming Teaching Based on Intelligent Conversation
This study first constructs a hypothesis model examining interactive factors in intelligent conversation-based programming instruction. Through questionnaire surveys and structural equation modeling, we validate the model's hypotheses. Results demonstrate that instructional interactions are primarily shaped by learning motivation, programming self-efficacy, and teacher factors, while intelligent technologies, teacher influence, and learning environments significantly impact students' motivation and programming self-efficacy. Finally, we propose recommendations for optimizing instructional interactions through intelligent technologies, teacher engagement, and environmental design to enhance the effectiveness of intelligent conversation-based programming education.
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