In today's rapidly evolving technology-focused societies, the traditional "teach" approach to computer science education, where knowledge is passively transferred from instructor to student, is increasingly insufficient. A more effective paradigm is "reach," which emphasizes active learning, problem-solving, and critical thinking. By providing students with opportunities to explore, experiment, and collaborate on real-world projects, educators can foster a deeper understanding of computer science concepts and inspire interdisciplinary collaborations. This shift from "teach" to "reach" is essential to equip the next generation with the skills and mindset necessary to thrive in a technology-driven future.

Text analysis has become an indispensable tool across numerous disciplines, enabling researchers to extract valuable insights from digitized documents. From analyzing literary texts to understanding social media trends, political discourse, and consumer behavior, text analysis empowers researchers to uncover hidden patterns, examine data, and improve decision-making. By automating tasks like sentiment analysis and topic modeling, text analysis can enhance efficiency and accelerate the pace of academic research. As a result, it continues to play a pivotal role in advancing knowledge and driving innovation in fields ranging from the humanities to the sciences.

Text analysis can be integrated into various Computer Science courses, from general education to specialized electives. In introductory courses, students can be introduced to basic text processing techniques, such as word counting and text segmentation. These techniques can be applied to simple text datasets, such as news articles or social media posts, to analyze sentiment, identify trends, or extract keywords. In more advanced courses, students can apply more sophisticated techniques, such as topic modeling and text classification. In data mining courses, students can learn to apply these techniques to large-scale datasets and develop advanced natural language processing systems. By incorporating text analysis into a variety of courses, educators can equip students with the skills to analyze and interpret textual data, which is essential for success in today's data-driven world. In this work, we report on three courses in our institutions, which incorporated text analysis into course content, assignments and projects. We describe student learning outcomes, measured over several years and showcase several real-word applications that resulted from these courses.

Keywords: Computer science education, text analysis.