Authors :
Hoàng Diệp Anh
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/44uy5adc
Scribd :
https://tinyurl.com/2s4642yh
DOI :
https://doi.org/10.38124/ijisrt/26apr1072
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This study examines how English majors at Ha Tinh University use smart devices in their learning, focusing on
their impacts, students’ self-regulation, and suggested strategies. Grounded in Bloom’s Taxonomy and Self-Regulated
Learning Theory, the research adopts a mixed-method approach, combining questionnaire data with in-depth interviews.
Findings indicate that smart devices have both positive and negative effects on learning outcomes. They effectively support
lower-order cognitive skills such as remembering and understanding, and facilitate the application of knowledge. They also
contribute to higher-order skills, including analyzing, evaluating, and creating. However, overreliance on tools such as
translation applications may hinder deep thinking. From the perspective of self-regulated learning, students demonstrate
some efforts in planning, monitoring, and reviewing their learning. Nevertheless, many struggle to manage distractions and
maintain consistent self-control. The study suggests several strategies to enhance effective use, including technical control
measures, structured learning approaches, and improved self-discipline. Overall, while smart devices offer significant
benefits, their effectiveness largely depends on students’ ability to regulate their own learning.
Keywords :
Smart Devices; Bloom’s Taxonomy; Self-Regulated Learning; Academic Performance; Distraction.
References :
- Piaget, J. (1972). The psychology of the child (H. Weaver, Trans.). Basic Books. (Original work published 1966)
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press
- Gökçearslan, Ş., Mumcu, F. K., Haşlaman, T., & Çevik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Computers in Human Behavior, 63, 639–649.
- Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
- Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals (Handbook I: Cognitive domain). Longmans, Green.
- Chen, Q., & Yan, Z. (2016). Does multitasking with mobile phones affect learning? A review. Computers in Human Behavior, 54, 34–42.
- Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. 2, pp. 215–239). Lawrence Erlbaum Associates.
- Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work. Educational Psychologist, 41(2), 75–86.
- Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning. Educational Psychology Review, 16(4), 385–407.
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online learning environments: A systematic review. Internet and Higher Education, 27, 1–13.
This study examines how English majors at Ha Tinh University use smart devices in their learning, focusing on
their impacts, students’ self-regulation, and suggested strategies. Grounded in Bloom’s Taxonomy and Self-Regulated
Learning Theory, the research adopts a mixed-method approach, combining questionnaire data with in-depth interviews.
Findings indicate that smart devices have both positive and negative effects on learning outcomes. They effectively support
lower-order cognitive skills such as remembering and understanding, and facilitate the application of knowledge. They also
contribute to higher-order skills, including analyzing, evaluating, and creating. However, overreliance on tools such as
translation applications may hinder deep thinking. From the perspective of self-regulated learning, students demonstrate
some efforts in planning, monitoring, and reviewing their learning. Nevertheless, many struggle to manage distractions and
maintain consistent self-control. The study suggests several strategies to enhance effective use, including technical control
measures, structured learning approaches, and improved self-discipline. Overall, while smart devices offer significant
benefits, their effectiveness largely depends on students’ ability to regulate their own learning.
Keywords :
Smart Devices; Bloom’s Taxonomy; Self-Regulated Learning; Academic Performance; Distraction.