Efektivitas Deep Learning terhadap Kemampuan Bernalar Kritis Siswa
DOI:
https://doi.org/10.29303/goescienceed.v6i4.1448Keywords:
deep learning, critical reasoning, higher-order thinking, 21st-century educationAbstract
This study aims to review the effectiveness of deep learning in enhancing students’ critical reasoning skills through a Systematic Literature Review (SLR) approach. Data were collected from Google Scholar, DOAJ, and Scopus databases covering publications from 2015 to 2025, using the keywords “deep learning,” “critical reasoning,” and “higher-order thinking.” A total of 20 selected articles were analyzed based on relevance, research design, and findings. The results indicate that deep learning effectively enhances students’ abilities to analyze, evaluate, and reflect by engaging them in meaningful and contextual learning processes. The most effective models include Problem-Based Learning (PBL), Project-Based Learning (PJBL), and Case-Based Learning, which foster collaboration and higher-order thinking. The success of deep learning implementation is also determined by the teacher’s role as a facilitator who cultivates a reflective and critical classroom culture. Therefore, integrating deep learning into instructional design is recommended as an innovative strategy to strengthen students’ critical reasoning and prepare them for 21st-century education challenges.
References
ndriani, N. (2023). The Influence Of Project-Based Learning Model On The Creativeness And Economic Learning Outcomes Of Students Of Class X Ips Sma Ylpi Pekanbaru In Academic Year. Peka, 10(2), 45–71. Https://Doi.Org/10.25299/Peka.2022.Vol10(2).11237
Dzakkiyah, A. A., Anggraini, I. S., Anjani, R., Chairani, S., & Mashudi, E. A. (2023). Analisis Bibliometrik: Penerapan Model Pembelajaran Blended Learning Di Paud. Kumarottama: Jurnal Pendidikan Anak Usia Dini, 3(1), 21–31. Https://Doi.Org/10.53977/Kumarottama.V3i1.1066
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble Deep Learning: A Review. In Engineering Applications Of Artificial Intelligence (Vol. 115). Https://Doi.Org/10.1016/J.Engappai.2022.105151
Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine Learning And Deep Learning Approaches For Cybersecurity: A Review. In Ieee Access (Vol. 10, Pp. 19572–19585).
Https://Doi.Org/10.1109/Access.2022.3151248
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep Learning For Time Series Classification: A Review. Data Mining And Knowledge Discovery, 33(4), 917–963. Https://Doi.Org/10.1007/S10618-019-00619-1
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine Learning And Deep Learning. Electronic Markets, 31(3), 685–695. Https://Doi.Org/10.1007/S12525-021-00475-2
Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey On Deep Learning With Class Imbalance. Journal Of Big Data, 6(1). Https://Doi.Org/10.1186/S40537-019-0192-5
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep Learning For Generic Object Detection: A Survey. International Journal Of Computer Vision, 128(2), 261–318. Https://Doi.Org/10.1007/S11263-019-01247-4
Lundervold, A. S., & Lundervold, A. (2019). An Overview Of Deep Learning In Medical Imaging Focusing On Mri. In Zeitschrift Fur Medizinische Physik (Vol. 29, Issue 2, Pp. 102–127). Https://Doi.Org/10.1016/J.Zemedi.2018.11.002
Manurung, A., & Marini, A. (2023). Penerapan Problem Based Learning Dalam Upaya Mengembangkan Kemampuan Berpikir Kreatif Mahasiswa. Jurnal Ilmiah Pendidikan Citra Bakti, 10(1), 142–154. Https://Doi.Org/10.38048/Jipcb.V10i1.967
Matsuo, Y., Lecun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep Learning, Reinforcement Learning, And World Models. Neural Networks, 152, 267–275. Https://Doi.Org/10.1016/J.Neunet.2022.03.037
Mohammed, A., & Kora, R. (2023). A Comprehensive Review On Ensemble Deep Learning: Opportunities And Challenges. In Journal Of King Saud University - Computer And Information Sciences (Vol. 35, Issue 2, Pp. 757–774). Https://Doi.Org/10.1016/J.Jksuci.2023.01.014
Muzakki, A., Ramadhanti, I. N., Alifiyan, I. N., & Ayu, T. S. (2022). Kajian Model Pembelajaran Fisika Sma Pada Topik Kinematika Gerak Lurus. Mitra Pilar: Jurnal Pendidikan, Inovasi, Dan Terapan Teknologi, 1(2), 85–98. Https://Doi.Org/10.58797/Pilar.0102.04
Oliveira, R. A. De, & Bollen, M. H. J. (2023). Deep Learning For Power Quality. In Electric Power Systems Research (Vol. 214). Https://Doi.Org/10.1016/J.Epsr.2022.108887
Shorten, C., & Khoshgoftaar, T. M. (2019). A Survey On Image Data Augmentation For Deep Learning. Journal Of Big Data, 6(1). Https://Doi.Org/10.1186/S40537-019-0197-0
Susilo, A., & Asmara, Y. (2020). Penerapan Model Pembelajaran Jigsaw Untuk Meningkatkan Hasil Belajar Ips. Yupa: Historical Studies Journal, 4(1), 20–28.




