Email this article Fostering Critical Writers: The Impact of Integrating Deep and Transformative Learning on Undergraduates’ Scientific Writing Competence
), Suhud Aryana(2), Aditya Permana(3), Hasbulloh Nadaraning(4)
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(1) Department of Indonesian Language and Literature Education, IKIP Siliwangi, Indonesia
(2) Department of Indonesian Language and Literature Education, IKIP Siliwangi, Indonesia
(3) Department of Indonesian Language and Literature Education, IKIP Siliwangi, Indonesia
(4) Department of Malay Language Teaching, Yala Rajabhat University, Thailand
Corresponding Author
Fostering Critical Writers: The Impact of Integrating Deep and Transformative Learning on Undergraduates’ Scientific Writing Competence. Objectives: This research aims to examine the effectiveness of integrating Deep Learning (DL) and Transformative Learning (TL) approaches in improving students’ scientific writing competence in general Indonesian language courses. Methods: A true-experimental design with pretest and posttest control groups was used because it provides strong control, ensuring that changes in outcomes are attributable to the DL–TL treatment. The population consisted of students in elementary school teacher education at IKIP Siliwangi, and 90 were selected via cluster random sampling. The experimental group (n=45) received DL–TL integrated instruction, while the control group (n=45) received conventional instruction. The research was conducted over 16 meetings from March 4 to August 10, 2025. Data were collected using a project-based scientific writing assessment supported by rubrics evaluating content relevance and originality, organization and coherence, argumentation quality, academic language use, and citation accuracy. Observation sheets and a Likert-scale questionnaire further supported the data. Inferential analysis employed the Shapiro–Wilk test, Levene’s test, and the independent t-test, while qualitative data were analyzed using the Miles and Huberman model. Findings: the experimental group achieved significantly greater improvement (Post-test Mean = 82.11) than the control group (Post-test Mean = 73.11), with a mean difference of 9.00 points (t(88)=11.112, p<0.001). The N-Gain Score of 0.7782 and N-Gain Percent of 77.8222 indicated a high and effective category of improvement. Qualitatively, students demonstrated increased engagement, reflective disposition, and collaboration. Conclusion: DL–TL integration proved more effective than conventional learning in enhancing scientific writing and strengthening students’ critical, reflective, and collaborative capacities.
Keywords: deep learning, transformative learning, scientific writing, indonesian language.
Abdelouahed, L. (2019). The use of e-learning in foreign language learning: A Case Study of Undergraduate EFL Students. International Journal of Language and Literary Studies, 1(3), 30–42. https://doi.org/10.36892/ijlls.v1i3.79.
Agustini, H., Nugraha, R. G., Hanifah, N., & Indonesia, U. P. (2024). Pengaruh penggunaan media pembelajaran padlet ULIK ( ular tangga interaktif kreatif ) terhadap hasil belajar IPAS siswa kelas IV [The influence of the use of ULIK (Creative Interactive Snakes and Ladders) Padlet learning media on the science learning outcomes of fourth grade students.]
Bakri, I., Wulandari, M. F., Amalia. S, R., & Rut, W. M. (2024). Quillbot integration in the learning of English writing skills (perception of business management students). International Journal of Research on English Teaching and Applied Linguistics, 5(1), 53–58. https://doi.org/10.30863/ijretal.v5i1.6412
Bal, M., & Öztürk, E. (2025). The potential of deep learning in improving K-12 students’ writing skills: A systematic review. British Educational Research Journal, 51(3), 1295–1312. https://doi.org/10.1002/berj.4120
Buchori, A., & Setyawati, R. D. (2015). Development learning model of character education through e-comics in elementary school. International Journal of Education and Research, 3(9), 369–386.
Buczkowski, P., Sobkowicz, A., & Kozlowski, M. (2018). Deep learning approaches towards book covers classification. ICPRAM 2018 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, 2018-Janua(Icpram), 309–316. https://doi.org/10.5220/0006556103090316
Creswell, J. W., & Creswell, J. D. (2018). Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). London (United Kingdom): SAGE Publications, Inc.
El-Sabagh, H. A. (2021). adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00289-4
Essa, S. G., Celik, T., & Human-Hendricks, N. E. (2023). Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: a systematic literature Review. IEEE Access, 11(April), 48392–48409. https://doi.org/10.1109/ACCESS.2023.3276439
Ferretti, R. P., & Graham, S. (2019). Argumentative writing: theory, assessment, and instruction. Reading and Writing, 32(6), 1345–1357. https://doi.org/10.1007/s11145-019-09950-x
Fleming, T. (2018). Mezirow and the theory of transformative learning. Critical Theory and Transformative Learning, 120–136. https://doi.org/10.4018/978-1-5225-6086-9.ch009
Frassetto, L. da S., Silva, I. N. da, Bilessimo, S. M. S., Machado, L. R. L. R., & Silva, J. B. da. (2022). Pedagogical models focused on the integration of ICT in basic education: A systematic review. International Journal of Advanced Engineering Research and Science, 9(8), 129–134. https://doi.org/10.22161/ijaers.98.16
Georgiou, T., Liu, Y., Chen, W., & Lew, M. (2020). A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision. International Journal of Multimedia Information Retrieval, 9(3), 135–170. https://doi.org/10.1007/s13735-019-00183-w
Gonçalves, S., Cortez, P., & Moro, S. (2018). A deep learning approach for sentence classification of scientific abstracts. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11141 LNCS, 479–488. https://doi.org/10.1007/978-3-030-01424-7_47
Gurung, S., Ghose, M. K., & Subedi, A. (2019). Deep learning approach on network intrusion detection system using NSL-KDD dataset. International Journal of Computer Network and Information Security, 11(3), 8–14. https://doi.org/10.5815/ijcnis.2019.03.02
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A systematic review of deep learning approaches to educational data mining. Complexity, 2019. https://doi.org/10.1155/2019/1306039
Hoggan, C., & Kloubert, T. (2020). Transformative learning in theory and practice. Adult Education Quarterly, 70(3), 295–307. https://doi.org/10.1177/0741713620918510
Krishna, S. T., & Kalluri, H. K. (2019). Deep learning and transfer learning approaches for image classification. International Journal of Recent Technology and Engineering, 7(5), 427–432.
Lee, I., Mak, P., & Yuan, R. E. (2019). Assessment as learning in primary writing classrooms: An exploratory study. Studies in Educational Evaluation, 62(November 2018), 72–81. https://doi.org/10.1016/j.stueduc.2019.04.012
Mirkhail, A. S., & Xinyou, Z. (2025). Deep learning for anomaly detection in loT healthcare systems. International Research Journal of Multidisciplinary Scope, 6(2), 1480–1494. https://doi.org/10.47857/irjms.2025.v06i02.03768
Mustaqim, A., Nurbaya, S., Mulyani, M., & Mazid, S. (2025). Developing a process-based learning module to enhance writing competence in journalism education. jurnal pendidikan progresif developing a process-based learning module to enhance writing, 15(04). https://doi.org/10.23960/jpp.v15i4.pp2273-2293
Qu, G., Hu, W., Jiao, W., & Jin, J. (2021). Application of deep learning-based integrated trial-error + science, technology, Reading/Writing, Engineer, Arts, Mathematics Teaching Mode in College Entrepreneurship Education. Frontiers in Psychology, 12(November). https://doi.org/10.3389/fpsyg.2021.739362
Schmohl, T., Watanabe, A., Fröhlich, N., & Herzberg, D. (2020). How can artificial intelligence improve the academic writing of students? The Future of Education, 12–14. https://thesiswriter.zhaw.ch/.
Sugiyono. (2019). Metode Penelitian Kuantitaif, Kualitatif dan R&B. Bandung: ALFABETA. CV.
Taylor, E. W. (2017). Critical reflection and transformative learning: a critical review. PAACE Journal of Lifelong Learning, 26(1990), 77–95.
Tsimane, T. A., & Downing, C. (2020). Transformative learning in nursing education: A concept analysis. International Journal of Nursing Sciences, 7(1), 91–98. https://doi.org/10.1016/j.ijnss.2019.12.006
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334
Winarni, E. W., Hambali, D., & Purwandari, E. P. (2020). Analysis of language and scientific literacy skills for 4th-grade elementary school students through discovery learning and ICT media. International Journal of Instruction, 13(2), 213–222. https://doi.org/10.29333/iji.2020.13215a
Wu, Y., & Schunn, C. D. (2021). The effects of providing and receiving peer feedback on writing performance and learning of secondary school students. American Educational Research Journal, 58(3), 492–526. https://doi.org/10.3102/0002831220945266
Wulan, C. A., Saputri, V. A. M., Paramita, S., Widiyanto, W., Gautama, S. A., & Ahmad, T. B. B. (2025). How does deep learning approach empowers extrovert students to excel in writing communications. Journal of Communication, Religious, and Social Sciences (JoCRSS), 3(1), 35–47. https://doi.org/10.60046/jocrss.v3i1.218
Zhang, D., Tan, J. T. A., & Roy, S. S. (2023). A systematic review of interventions improving university students’ EFL writing competence. International Journal of Learning, Teaching and Educational Research, 22(10), 93–112. https://doi.org/10.26803/ijlter.22.10.6
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