| Popis: |
Abstract Research on language teaching quality has certainly stood out enough to be noticed as the momentum higher school teaching change proceeds to extend and develop. The way to further developing language teaching quality is to further develop teaching quality, and educator assessment is a significant instrument for doing as such. Accordingly, school administration necessitates the turn of events and refinement of a framework for assessing language teaching quality. Thus, hybrid learning technique for assessing the teaching quality of high school instructors should be created. We present an interesting model for assessing the quality of homeroom teaching involving artificial intelligence innovation in high schools, which depends on better hereditary calculations and neural networks. The fundamental thought is to utilize higher request factual elements (skewness, change, second and kurtosis), even vulnerability, Improved Independent Component Analysis (IICA), Holo-entropy based highlights to remove the underlying loads and limits of gathered data. The teaching quality assessment results were enhanced by further developing the neural network’s forecast accuracy and intermingling speed, bringing about a more down to earth plot for assessing high school language teaching quality. We have led simulation investigations and comparative analysis utilizing the Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (ConvNet/CNN) models. Then, an education quality assessment framework is laid out by hybrid optimizing model parameters which is Seagull Optimization Algorithm (SOA) and Red Colobuses Monkey (RCM). |
Full text from SpringerLink