Abstract

William Shakespeare’s "Hamlet," written around 1600, is one of the most famous tragedies in the English language. The play is rich in themes such as revenge, madness, mortality, and the complexity of the human condition.                   

This paper analyzes the dramatic structure using Freytag’s pyramid and Aristotle tragedy theory as a framework. They examine the play’s exposition, inciting incident, rising action, climax, falling action, resolution, and conflict combining with Aristotle theory to understand how Shakespeare constructs a powerful tragedy. The method used in this study is a descriptive qualitative, which aims to analyzing and collecting data from the play and some referential books, library ,websites and other data sources that were useful for the analysis.The study also explores the significance of Hamlet in literary studies. The results of the study have pedagogical implications for teaching English literature for students and language learners. 

Key words: Hamlet ,Aristotle theory, Freytag pyramid, tragedy drama. 

Abstract: In light of the aspiration to achieve sustainability in the field of education, especially after the development of artificial intelligence technologies in general and artificial neural network technologies in particular, the process of improving e-learning has become one of the most important strategies for achieving sustainability. This study aims to evaluate the impact of using artificial intelligence techniques and artificial neural networks on improving cloud e-learning by extracting and mining data from the electronic cloud and analyzing it to provide a more efficient and effective educational experience. Through several methodologies, including descriptive methodology to describe the factors influencing the improvement of machine learning through the use of artificial intelligence techniques and the mechanism for using these techniques, quantitative methodology was used in collecting data and analytical scientific methodology to analyze the results of the proposed model, which is a hybrid of convolutional artificial neural networks (CNN) and recursive neural networks (RNN). Through the model, it is possible to extract and analyze user data such as interaction with content and duration of study to extract patterns and behaviors that support improving the learning experience, and personalizing individual learning paths. Predicting the academic performance of students using machine learning techniques. The results showed an improvement in the learning experience of 86% and an improvement in platform performance of 85%. Machine learning indicators, such as accuracy (94%), recall (93%) and f1 score (91%), also indicated the success of the model used in