An Evaluation Model of Tecaching Assistant using Artificial Neural Network
Nowadays, most of the academic institutes facing a low-quality problem in the educational field. One of these factors is an educational student achievement and staff teaching quality. This study presents an efficient framework for assessment and prediction of teachers’ performance in academic institutes using Artificial Neural Network (ANN) algorithm. The proposed framework designed to predict the performance quality level of teachers in order to improve the learning outcomes. The prediction model was tested effectively using the TA UCI dataset. The data consists of academic experiences for teachers as well as their experiences and grades of students in courses they taught among others. The SPSS tool was used to build the suggested prediction system. The TA data was dividing into three groups (70, 80, and 90) for training data, and (30, 20, and 10) for testing data respectively to study the dataset discrimination. The results are showing that the neural network obtained better accuracy results with (90%) in the training and (10%) in testing.
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