Abstract
During COVID-19, a blended approach to online and offline interactive teaching and learning will be a primary mobile teaching methodology. The effectiveness of online and offline interactive blended learning models depends heavily on the evaluation and optimization of the quality of instruction. However, current evaluation methods often lack systematicity and accuracy, and cannot fully reflect the actual performance and learning effects of students in the interactive blended learning mode. To optimize student learning in this mobile mode, it is most important to adhere to the basic design principles, utilize micro-learning resources and establish a mobile online teaching platform. Therefore, a comprehensive evaluation system and a hybrid teaching quality evaluation model based on back propagation (BP) neural network were proposed in the study. The new model innovatively combined BP neural network and genetic algorithm (GA) to propose a GA-BP optimization model. Subsequently, the accuracy and dependability of teaching quality evaluation were successfully increased by the study’s establishment of a thorough hybrid mobile system. The outcomes revealed superior student performance in the hybrid teaching mode compared to traditional methods, with an increase in the number of students achieving high scores. Among the four evaluation models (GA, BSA, BP, and GA-BP), GA-BP demonstrated the closest alignment with original grades. It yielded mean error and mean relative error of 3.78 and 0.03, respectively, representing the smallest discrepancies. These findings underpinned the efficacy of the blended instructional model in enhancing student learning outcomes. Moreover, the GA-BP-based mobile evaluation model was more accurate in assessing the quality of instruction, thus providing a more effective evaluation.
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