This research aims to explore the application of Big Data analysis in understanding university student experiences. With the increasing globalisation and economic challenges facing higher education, it is crucial to enhance student satisfaction and outcomes to maintain institutional competitiveness. Traditional methods of gathering student feedback, such as surveys, can have issues such as low response rates and bias. Therefore, leveraging Big Data from platforms like RateMyProfessor.com presents an underexplored yet potentially transformative approach to gaining insights into student experiences. Below are the research questions:

• What does Big Data analysis reveal about what is important to students undertaking university courses?
• What is the best use of data analysis techniques for analysing big qualitative datasets about student experiences? The research employs the Student Experience Analysis (SEA) system, which integrates advanced Big Data analysis techniques, including Natural Language Processing (NLP). The methodology involves data pre-processing steps such as data cleaning, tokenisation, lemmatisation, and word clustering. The analysis focuses on extracting keywords and sentiment and categorising student comments. The research dataset is sourced from public reviews on RateMyProfessor.com, and the results are compared with existing literature on student experiences. The analysis identified the most frequent keyword clusters in student feedback. The findings indicate that student experiences are deeply influenced by teaching quality, support services, curriculum design, and assessment methods. Positive experiences are linked to clear communication, engaging teaching methods, and accessible support services, while negative experiences often stem from high perceived difficulty and lack of support. These findings suggest that Big Data analysis can effectively complement traditional survey methods by providing more comprehensive and timely insights into student experiences. This approach can help universities better understand and address the factors that influence student satisfaction and retention. By integrating Big Data techniques, institutions can enhance their educational offerings, improve student outcomes, and ultimately maintain their competitiveness in a globalised education market. This research underscores the potential for technological innovations to transform qualitative data analysis in higher education, paving the way for more responsive and data-driven decision-making.