Research on Human-Machine Hybrid Enhanced Programming Teaching Model

Human-machine hybrid enhanced intelligence, as a cutting-edge technology in the advancement of artificial intelligence, can provide dynamic support for innovative developments in intelligent education characterized by human-machine collaboration. Addressing the prevalent issue in current university programming courses where learning feedback relies excessively on either the teacher or machine, it is of great theoretical and practical significance to explore the tripartite composite subject hybrid enhanced teaching model. Based on the principles of human-machine hybrid-enhanced intelligence, Robert Gagne’s instructional process, and perspectives on learning feedback, a programming teaching model of "teacher-machine-student" hybrid enhancement is proposed. This model was applied to a C language course at a certain university, involving 141 students from three classes. The aim is to provide insights into the application of artificial intelligence technologies in programming education. The results demonstrate that the proposed model accurately identifies issues in programming teaching and promptly delivers personalized learning feedback, thereby enhancing teaching effectiveness.