Deep Learning-Based Assessment of Teacher Effectiveness in classrooms: Integrating ARIMA and LSTM Models

Teachers are no different from everyone else when it comes to the value of helpful criticism. Because the system teach so often, teachers have plenty of chances to perfect these skills. However, improving one's abilities via unstructured exercise alone isn't enough; targeted practice, usually with the help of a coach, may be necessary for mastery. The ideal coach would provide instructors with realistic yet demanding training goals, provide them with helpful feedback, and help them find ways to advance their careers. The proposed approach incorporates preprocessing, feature selection, and training of the model. Data cleaning, conversion, and formatting are all part of data preparation, which is preparing a dataset for analysis. Data gain and principal component analysis make up feature selection. The ARIMA-LSTM model was consistently used throughout the training. With a 90.86% average accuracy, this innovative method surpasses ARIMA and LSTM.