Analyzing Teacher Stress and Self Efficacy in Classroom Management with Fast Iterative Shrinkage Thresholding ELM Models

The last decade has seen a proliferation of research investigating the link between teachers' levels of stress and their confidence in their own abilities. Research on teachers' self-efficacy, stress, engagement, school environment, and health has recently emerged, and this article reviews the most recent research that have explored these relationships. Teachers' stress levels and self-efficacy are found to have varied predictive powers of their cognitive, emotional, and behavioral reactions, according to research.The three-step procedure that constitutes the suggested method are preprocessing, feature selection, and model training. Text cleaning, tokenization, lemmatization, stop word removal, data reduction, and transformation are some of the preprocessing steps used to prepare the OESF. Performance, processing costs, the "dimensional curse," and data comprehension are all improved through feature selection, which is also known as variable or attribute reduction. The model was trained using FISTA-ELM. With a 93.65% average accuracy rate, this state-of-the-art approach beats FISTA and ELM.