<![CDATA[This study examines how university students make sense of deep learning in the process of writing argumentative essays. The research focuses on students’ experiences, their level of understanding, and their views regarding the use of this learning approach in writing activities. A descriptive qualitative method was employed, with participants selected purposively based on their prior involvement in deep learning practices. Data were gathered using a questionnaire that combined closed-ended and open-ended questions, allowing the researcher to capture both general patterns and more detailed insights. The analysis was conducted through several stages, including data reduction, data display, and drawing conclusions. The findings show that most students perceive deep learning as an approach that promotes thorough understanding, encourages active participation, and supports the development of more organized and coherent arguments. In addition, many students report that this approach helps them generate ideas more effectively and improves the overall quality of their argumentative writing. Despite these positive responses, some students still encounter challenges, particularly in grasping key concepts and adapting to a learning process that requires greater independence and active engagement. These difficulties suggest that not all students are equally prepared for this type of learning environment. Overall, the study indicates that deep learning has strong potential to enhance students’ argumentative writing skills. However, its implementation should take into account students’ readiness, learning habits, and individual differences to ensure more optimal outcomes.]]>
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