The Effectiveness of A Deep Learning Integrated E-Comic in Enhancing Students’ Reading of Descriptive Text

Abstract

This study investigates how well students' reading comprehension of descriptive texts is enhanced by an e-comic that incorporates deep learning. The study used a pre-test–post-test control group and a quasi-experimental design. Students from SMPN 3 Bireuen participated in the study; they were split into two groups: an experimental group and a control group. While the control group received traditional reading training, the experimental group was taught using an e-comic that combined deep learning. A reading comprehension exam was containing five indicators—identifying core concepts, comprehending supporting information, vocabulary comprehension, drawing conclusions, and detecting descriptive text structure—was used to gather data. Descriptive statistics and an independent samples t-test were used to assess the data. The results demonstrated that following the treatment, both groups showed improvement. The experimental group continuously scored better than the control group in every reading feature, despite the control group showing a greater numerical rise in overall scores. According to the N-gain analysis, the experimental group outperformed the control group (0.44, medium category) in terms of improvement (0.67, high category). Additionally, the findings showed that the two groups differed statistically significantly (p < 0.05). These results imply that incorporating deep learning into e-comic content improves students' reading comprehension, especially in terms of word comprehension and inferential abilities. Thus, in EFL situations, deep learning-integrated e-comics can be a creative and useful teaching tool.