基于协同LSTM-SVR模型的英语课程微信辅助教学成绩预测

Prediction of Student Grades in College English WeChat Teaching Based on Collaborative LSTM-SVR Model

近年来随着国内外移动互联网络科技水平的不断进步,特别是微信小程序这一新兴产业的出现及推广,为了优化微信小程序的辅助英语教学模式以及为实施教学干预提供数据支持,使用协同LSTM-SVR模型,通过学习过程评价结果预测学生的期末成绩,指导教师针对性地根据过程考核预测的期末成绩调整教学模式和策略.结果表明,预测的微信小程序辅助教学考核成绩有良好的准确率,有助于反映辅助教学效果及时调整教学.

In recent years,with the continuous advancement of mobile internet technology both domestically and internationally,especially the emergence and promotion of the emerging industry of WeChat mini programs,in order to optimize the auxiliary English teaching mode of WeChat mini programs and provide data support for implementing teaching interventions,a collaborative LSTM-SVR model is used to predict studentsfinal grades through learning process evaluation results,guide teachers to adjust teaching modes and strategies based on the predicted final grades of the process assessment.The results indicate that the predicted WeChat mini program as-sisted teaching assessment results have good accuracy,which helps to reflect the effectiveness of assisted teaching and adjust teaching in a timely manner.