The Application of Hybrid Model in College English Translation Teaching
In order to solve the problem of lack of coherence in the design of online and offline tasks in the current blended learning model,this study combines task-based teaching theory and blended learning mode,constructs a task-based blended model,and applies it to college English translation teaching.In order to better evaluate the implementation effect of the model,this study focuses on the evaluation of student translation,proposes evaluation indicators based on pre training models and contrastive learning,and constructs a teaching quality evaluation model based on GA-BP neural network.The research results indicate that the average overall learning level of the class using the hybrid model increased by 40.01%after the end of the entire semester,which is 10.54%higher than the benchmark class,indicating that students using the task-based hybrid teaching model achieved greater improvement in translation skills.The experimental results have demonstrated the practicality and superiority of the hybrid model proposed in the study.The neural network model based on GA-BP has a high prediction accuracy of 97.3%for student grades,which is conducive to judging the applicability of blended learning mode to college English translation teaching and promoting the adjustment and improvement of college English translation teaching.
blended learningtask based teachingEnglish translationGA-BPteaching quality evaluation
NETL
NSTL
万方数据
