Personalized learning effect evaluation model for vocational education with cloud computing technology

The advent of cloud computing technology (CCT) has expedited the advancement of online learning methodologies and, to a certain extent, compensated for the limitations inherent in traditional teaching approaches. However, online teaching under CCT still has the problem of unstable teaching quality, so the study establishes a relevant learning effect evaluation model for the personalized learning platform of vocational education under CCT. To achieve more efficient and accurate evaluation of learning effect, an adjustable variation genetic algorithm-backpropagation neural network (AGA-BP) is proposed. The model introduces an adjustable mutation approach, which adapts the mutation probability in real-time in accordance with the progress of the genetic algorithm in the search process, so as to prevent entering into local optimization and ensure the maintenance of diversity. This strategy significantly enhances the convergence speed and overall search capability of the algorithm. Meanwhile, using the excellent fitting characteristics of neural network, AGA-BP model can accurately learn and simulate different students' learning behavior and effectiveness. The experiment outcomes indicate that the model's mean square error is 3.3883e*10-12, its fitness value is 1.36, and its average accuracy is 98.35 %.