Deep Learning Approach in Argumentative Writing to Improve Critical Thinking and Rhetorical Structure

Abstrak

This study aims to analyze the effect of the application of a deep learning approach in a argumentative writing instruction on the critical thinking skills and the quality of the rhetorical structure of students' writing in the Indonesian Language and Literature Education Study Program at Universitas Negeri Makassar. This study used a quantitative approach with a quasi-experimental design involving two groups, namely the experimental class and the control class. The research sample consisted of 70 students consisting of 35 students in the experimental class and 35 students in the control class. Data collection techniques were carried out through argumentative writing tests and critical thinking ability tests carried out at the pretest and posttest stages. The data obtained were analyzed using inferential statistical analysis through an independent sample t-test. The results of the study showed that the application of the deep learning approach had a significant effect on improving students' argumentative writing skills. This was indicated by an increase in the average score in the experimental class which was higher than the control class. In addition, students in the experimental class showed better abilities in compiling the rhetorical structure of the text, which includes claims, reasons, evidence, and conclusions more systematically. The findings of this study indicate that the deep learning approach can encourage students to think more critically and develop arguments more logically and structured in academic writing. Thus, this approach can be an effective learning strategy to improve the quality of writing learning in higher education.