A Study on Designing Multi-Level Computing Models to Enhance the Effectiveness of Computer Education by Combining Generative AI Techniques

In today's era, the transformative power of computing is highlighted, and computational thinking has become the core literacy and essential ability of learners, while computer education is an effective carrier for cultivating computational thinking.The article firstly researches the theory related to collaborative filtering and generative adversarial recommender system.Then it combines SeqGAN with traditional CF algorithms, proposes to use sequence generative adversarial network for missing data prediction, and makes appropriate improvements to SeqGAN to make it suitable for generating scoring data, and then further designs a computer teaching system based on this model.The article launches performance testing experiments on Ali's real dataset UserBehavior, and conducts experiments on the effect of computer education with the students of computer application major in a secondary school as the research object.The results of the study show that in the comparative analysis of the pre-test and post-test of computational thinking of the experimental class, the mean of the total score of computational thinking of the experimental class in the pre-test and post-test is 71.17 and 78.35, respectively, and the post-test is more than 7 points higher than the pre-test.It can be concluded that the teaching model of multilevel computational modeling designed in this paper promotes the development of students' computational thinking and academic performance, improves students' learning attitudes, and increases classroom participation.