Natural language processing (NLP) developments have made it possible for robots to read and analyze human language with astounding precision, revolutionizing the field of text understanding. An overview of current advancements in NLP approaches and their effects on text comprehension are provided in this abstract. It examines significant developments in fields including named entity identification, sentiment analysis, semantic analysis, and question answering, highlighting the difficulties encountered and creative solutions put forth. To sum up, recent developments in natural language processing have raised the bar for text comprehension. Deep learning models and extensive pre-training have changed methods including semantic analysis, sentiment analysis, named entity identification, and question answering. These developments have produced text comprehension systems that are increasingly precise and complex. However, issues with prejudice, coreference resolution, and contextual comprehension still need to be resolved. The future of NLP for text understanding has considerable potential with continuing study and innovation, opening the door for increasingly sophisticated applications in numerous sectors.

The absence of a universal and useful framework for directing the integration of flipped classrooms is one of the main obstacles inhibiting teachers from implementing flipped learning in their teaching practices. This unclear framework results in the reality that the efficacy of the flipped learning strategy is still unknown. This study presented the “ADDIE Paradigm,” a step-by-step generic model based on learning and teaching research findings. The study involved two cohorts of 60 first-year college students. The experimental group used ADDIE for five weeks, while the control group received instruction using the flipped model. The findings confirmed the viability of the ADDIE integrated flipped learning paradigm, which not only enhanced student engagement and teaching quality but also helped them perform better on exams. Future research topics, as well as instructional ramifications for online learning, are highlighted. Thus, the findings of this study can reinforce the theoretical foundations for flipped learning and aid in their acceptance in actual teaching.

The purpose of this research paper is  intended to look into how teacher-mediated flipped learning and student-regulated learning affect presentation skills. A total of 68 English as Specific Language (ESL) students were chosen for the intervention trial. In Group-A, 34 students were exposed to teacher-mediated flipped learning, whereas Group-B students were exposed to student-regulated flipped learning. Before the intervention, the samples were homogeneous. To determine the impact, the researchers used Levene’s Test of Variance. The results of this study clearly illustrate that both teacher-mediated and student-regulated learning brings potential and obstacles. Both groups showed signs of progress. Students in the teacher-mediated flipped mode, on the other hand, outperformed those in the self-regulated flipped method. Keywords- ESL,  Flipped Instruction, Online Learning, Pedagogy, Self-Regulated Learning