The Effectiveness of A Deep Learning Integrated E-Comic in Enhancing Students’ Reading of Descriptive Text
DOI:
https://doi.org/10.21009/jtp.v28i1.66497Keywords:
Deep Learning, Integrating, E-Comic, ReadingAbstract
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.
References
Arief, Z. A., Mujahidin, E., & Hartono, R. (2022). The Effect of Digital Comic Media on East Asian Student s’ English Language Learning Outcomes.
Audrin, C., & Audrin, B. (2022). Key factors in digital literacy in learning and education: a systematic literature review using text mining. In Education and Information Technologies (Vol. 27, Issue 6). https://doi.org/10.1007/s10639-021-10832-5
Berube, L., Wisdom, S., & Cooke, I. (2024). “ Moving with the story ”: the haptics of reader experience and response to digital comics. 1–91.
Case, S. L. (2023). applied sciences Cognitive Load Approach to Digital Comics Creation : A.
Damayanti, F., & Supriyatin, T. (2022). Implementasi Educational Comic Berbasis Aplikasi Comic Life sebagai Media Pembelajaran Alternatif. Jurnal ABDINUS : Jurnal Pengabdian Nusantara, 6(2), 365–373. https://doi.org/10.29407/ja.v6i2.16551
Education, H., Article, I. T., Kumar, R., & Dhar, D. (2024). A think - aloud study : Exploring the effects of digital vs . print comics on reading efficiency and comprehension. 1–11.
Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121(April), 1–9. https://doi.org/10.1016/j.system.2024.103254
Huang, B., Dou, J., Zhao, H., & International, S. (2023). Reading bots : The implication of deep learning on guided reading. 1–61.
Information, P., Program, S., Semarang, U. N., & Sciences, N. (2025). E-Comic Assisted by Articulate Storyline Problem-Based in Integrated Deep Learning of Semarang ’ s Typical Culinary for Problem-Solving Skills. 2–6.
Journal, I., Technology, E., & Education, H. (2021). Adaptive e - learning environment based on learning styles and its impact on development students ’ engagement. 1–48.
Kukreja, V. (2024). Comic exploration and Insights : Recent trends in LDA-Based recognition studies. 255(December), 1–10.
Liao, S., Yu, L., Kruger, J., & Reichle, E. D. (2024). Dynamic reading in a digital age : new insights on cognition. 28(1), 1–6.
Muhlisin, A. (2023). E-Comic Science Integrated with PBL Model to Improve Problem Solving Skills Discover the world ’ s research. 1, 2–5. https://doi.org/10.29303/jppipa.v9i1.1676
Nguyen, N., Rigaud, C., & Burie, J. (2018). Digital Comics Image Indexing Based on Deep Learning. 11.
Peng, J., Zhou, W., Han, Y., Li, M., & Liu, W. (2023). Deep learning-based autonomous real-time digital meter reading recognition method for natural scenes. 222(November), 1–8.
Roomy, M. A. Al. (2022). Investigating the Effects of Critical Reading Skills on Students’ Reading Comprehension. 13(March), 366–381.
Shabrina, A., & Astuti, U. P. (2022). The Integration of 6Cs of the 21st Century Education into English Skills: Teachers’ Challenges and Solutions. Jurnal Pendidikan: Teori, Penelitian, Dan Pengembangan, 7(1). https://doi.org/10.17977/jptpp.v7i1.15185
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. Computers and Education: Artificial Intelligence, 9(December), 1–39. https://doi.org/10.1016/j.caeai.2025.100429
This, C. (2021). Contextual-Based E-comic Media Design. 2–3. https://doi.org/10.1109/URICET53378.2021.9865903
Zhang, G. (2024). English Reading and Learning Platform using Artificial Intelligence, Deep Learning and Micro Services. 2024 1st International Conference on Software, Systems and Information Technology, SSITCON 2024, 2024–2025. https://doi.org/10.1109/SSITCON62437.2024.10797018
Zuraini, Z., Hayati, R., Meilinar, F., & Marzuki, M. (2024). The Effect of Digital Story Telling on Students’ Reading Literacy to EFL Learning. Ilomata International Journal of Social Science, 5(4), 1261–1270. https://doi.org/10.61194/ijss.v5i4.1443
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