Technology-Based Learning Enhances Self-Regulated Learning: Systematic  Literature Review

Ahmad Tohir; Arimbi Pamungkas; Agnes Fibriana Kurniawati

doi:10.37676/sosj.v3i2.2019

Authors

Ahmad Tohir Sekolah Tinggi Keguruan dan Ilmu Pendidikan Al Islam Tunas Bangsa
Arimbi Pamungkas Sekolah Tinggi Keguruan dan Ilmu Pendidikan Al Islam Tunas Bangsa
Agnes Fibriana Kurniawati Universitas Katolik Musi Charitas

DOI:

https://doi.org/10.37676/sosj.v3i2.2019

Keywords:

Learning Independence, Technology-Based Learning, Systematic Literature Review

Abstract

The global transformation of the digital education ecosystem demands a high level of independent learning from students to navigate expanding academic autonomy. This systematic literature review aims to identify, evaluate, and synthesize the effectiveness of implementing cutting-edge instructional technologies on improving students' self-regulation strategies across academic levels. This research approach adopted the rigorous PRISMA selection protocol by extracting purely primary empirical literature from the Scopus database covering publications from 2016 to 2026, resulting in fifteen final articles for in-depth thematic analysis. The analysis findings demonstrate that the presence of adaptive learning platforms, artificial intelligence generative feedback, gamified virtual reality devices, and smart e-books successfully trigger a surge in students' metacognitive capacity and positive affect. These digital devices consistently replace the role of manual instructor supervision by providing instant cognitive scaffolding that can calibrate task difficulty based on students' actual performance track records. The study concludes that the modern technology ecosystem is not simply a passive repository of material but has evolved into a proactive co-regulatory agent that fundamentally transforms academic cognitive load into long-term intrinsic persistence. The novelty of this research rests on a theoretical unpacking of the psychological interactions of humans and machines, providing conclusive evidence of how personalized digital algorithms are able to hijack the brain's gratification system to catalyze self-discipline, while providing a crucial guiding framework for designing hybrid pedagogical curricula in the future.

References

Akour, I. A. (2022). A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Computers and Education Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100052

Altunay, H. C. (2023). A hybrid CNN + LSTM based intrusion detection system for industrial IoT networks. Engineering Science and Technology an International Journal, 38. https://doi.org/10.1016/j.jestch.2022.101322

Berglas-Shapiro, T. (2017). A technology-enhanced intervention for self-regulated learning in science. Teachers College Record, 119(13). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044388607&origin=inward

Cárdenas-Robledo, L.A. (2019). A holistic self-regulated learning model: A proposal and application in ubiquitous learning. Expert Systems with Applications, 123, 299–314. https://doi.org/10.1016/j.eswa.2019.01.007

Chen, C. (2024). Effects of a self-regulated-based gamified interactive e-book on primary students' learning performance and affection in a flipped mathematics classroom. Education and Information Technologies, 29(18), 24143–24180. https://doi.org/10.1007/s10639-024-12789-7

Dong, X. (2022). A lightweight vehicles detection network model based on YOLOv5. Engineering Applications of Artificial Intelligence, 113. https://doi.org/10.1016/j.engappai.2022.104914

Du, J. (2025). Empowering Students in Online Learning Environments Through a Self-Regulated Learning–Enhanced Learning Management System. Behavioral Sciences, 15(8). https://doi.org/10.3390/bs15081041

Elmabaredy, A. (2024). Exploring the integration of self-regulated learning into digital platforms to improve students' achievement and performance. Discover Education, 3(1). https://doi.org/10.1007/s44217-024-00233-4

Fang, U. (2023). A Comprehensive Survey on Multi-View Clustering. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12350–12368. https://doi.org/10.1109/TKDE.2023.3270311

Garg, S. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. https://doi.org/10.1108/IJPPM-08-2020-0427

Hassani, S. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Censorship, 23(4). https://doi.org/10.3390/s23042204

Jafari, M. (2023). A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future. IEEE Access, 11, 17471–17484. https://doi.org/10.1109/ACCESS.2023.3241588

Kasongo, SM (2023). A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Computer Communications, 199(Query date: 2026-09-29 23:31:18), 113–125. https://doi.org/10.1016/j.comcom.2022.12.010

Kuwaiti, A. A. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine, 13(6). https://doi.org/10.3390/jpm13060951

Li, P. (2023). A comprehensive survey on design and application of autoencoders in deep learning. Applied Soft Computing, 138. https://doi.org/10.1016/j.asoc.2023.110176

Lin, C. H. (2026). The impact of Taiwan adaptive learning platform (TALP) on self-regulated learning and mathematics achievement. Educational Psychology, 46(3), 437–458. https://doi.org/10.1080/01443410.2025.2561028

Liu, Y. (2023). A Survey on Evolutionary Neural Architecture Search. IEEE Transactions on Neural Networks and Learning Systems, 34(2), 550–570. https://doi.org/10.1109/TNNLS.2021.3100554

Lowe, M. (2022). A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. Water Switzerland, 14(9). https://doi.org/10.3390/w14091384

Muruganantham, P. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sensing, 14(9). https://doi.org/10.3390/rs14091990

Ng, D.T.K. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454

Ouyang, Z. (2025). Self-regulated learning and engagement as serial mediators between AI-driven adaptive learning platform characteristics and educational quality: A psychological mechanism analysis. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1646469

Pan, Y. (2025). Promoting students' metacognition and self-regulatory efficacy through metacognitive scaffolding in mobile technology-enhanced interactive classrooms: The moderating role of inner speech. Education and Information Technologies, 30(8), 10811–10836. https://doi.org/10.1007/s10639-024-13284-9

Rakhmetov, M. (2025). Evaluation of an AI-Based Feedback System for Enhancing Self-Regulated Learning in Digital Education Platforms. Electronic Journal of E Learning, 23(4), 126–141. https://doi.org/10.34190/ejel.23.4.4150

Saheed, Y. K. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12), 9395–9409. https://doi.org/10.1016/j.aej.2022.02.063

Sergent, T. (2024). How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application? IEEE Transactions on Learning Technologies, 17, 1184–1197. https://doi.org/10.1109/TLT.2024.3360121

Shyr, W. J. (2018). Designing a technology-enhanced flipped learning system to facilitate students' self-regulation and performance. Journal of Computer Assisted Learning, 34(1), 53–62. https://doi.org/10.1111/jcal.12213

Singh, S. (2022). A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Future Generation Computer Systems, 129, 380–388. https://doi.org/10.1016/j.future.2021.11.028

Susantini, E. (2021). E-book of metacognitive learning strategies: Design and implementation to activate student's self-regulation. Research and Practice in Technology Enhanced Learning, 16(1). https://doi.org/10.1186/s41039-021-00161-z

Verdecchia, R. (2023). A systematic review of Green AI. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 13(4). https://doi.org/10.1002/widm.1507

Zhao, J. H. (2024). Effects of a self-regulated-based gamified virtual reality system on students' English learning performance and affection. Interactive Learning Environments, 32(9), 5509–5536. https://doi.org/10.1080/10494820.2023.2219702

Zhidkikh, D. (2023). Measuring self-regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials. Journal of Computer Assisted Learning, 39(6), 1834–1851. https://doi.org/10.1111/jcal.12842