IJLRET Menu
Current Issue [Vol. 11, No. 10] [October 2025]
- Citation
- Abstract
- Reference
- PDF Download
| Paper Title | :: | Digital Tools and Personalization in the Learning Process |
| Author Name | :: | Miroslava Mihajlov Carević |
| Country | :: | Serbia |
| Page Number | :: | 01-04 |
Modern technologies involved in the education process have led to significant changes in the approach to the educational process. The development of information and communication technologies has enabled the emergence of various digital tools that can contribute to easier and more efficient transfer and acquisition of knowledge. The purposeful integration of digital tools into the teaching and learning process is a current issue in the education system. The fact is that students have different levels of prior knowledge and motivation, which gives digital tools the opportunity to contribute to the quality of education when used appropriately. In this paper, we analyze the use of personalized tools in the teaching and learning process.
Keywords: digital tools, personalization, teaching, learning.
Keywords: digital tools, personalization, teaching, learning.
[1]. Rothwell, W. J., Zaballero, A., Sadique, F., & Bakhshandeh, B. (2024). Revolutionizing the Online Learning Journey: 1,500 Ways to Increase Engagement. Productivity Press.
[2]. Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623-2656.
[3]. Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684.
[4]. Ou, C. (2024). Designing socially dynamic digital learning: Technologies and strategies for student engagement. Routledge.
[5]. Nguyen, Q., Rienties, B., & Whitelock, D. (2022). Informing learning design in online education using learning analytics of student engagement. Open world learning: research, innovation and the challenges of high-quality education, 189-207.
[2]. Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623-2656.
[3]. Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684.
[4]. Ou, C. (2024). Designing socially dynamic digital learning: Technologies and strategies for student engagement. Routledge.
[5]. Nguyen, Q., Rienties, B., & Whitelock, D. (2022). Informing learning design in online education using learning analytics of student engagement. Open world learning: research, innovation and the challenges of high-quality education, 189-207.