1Vannam LE, 2Bao Nam TRAN, 3Thi Thuy Ngan NGUYEN, 4Hai Linh NGUYEN, 5Thi Thu Phuong DUONG, 6Ngoc Mai HOANG
1,2,3,4,5,6National Economics University, Hanoi, Vietnam
DOI : https://doi.org/10.47191/ijmra/v8-i03-65Google Scholar Download Pdf
ABSTRACT:
In today’s technology-driven academic environments, digital distraction has become a critical challenge to student concentration and learning effectiveness. This study investigates the psychological, behavioral, and contextual factors influencing digital distraction among university students. Drawing on Cognitive Load Theory, Self-Regulation Theory, and Attention Control Theory, the study develops and tests a comprehensive structural model incorporating Technology Usage Frequency, Cognitive Load, Emotional State, Self-Regulation Ability, and Social Media Engagement, with the Classroom Environment as a moderator. Data were collected from 508 valid responses via an online survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Technology Usage Frequency and Cognitive Load are key predictors of digital distraction, while Self-Regulation Ability serves as a protective factor. Emotional State and Social Media Engagement also contribute to distraction, although the link between social media and emotional state was not statistically significant. Furthermore, the Classroom Environment significantly moderates several relationships, amplifying or buffering their effects on distraction. The model explains 47.1% of the variance in digital distraction, offering strong empirical support for its explanatory power. The study provides theoretical insights into the interplay between internal and external influences on attention and delivers practical recommendations for designing distraction-resistant learning environments.
KEYWORDS:Digital distraction; Cognitive load; Self-regulation; Emotional state; Technology use; Social media engagement; Classroom environment.
REFERENCES1) Aagaard, J. (2021). ‘From a small click to an entire action’: exploring students’ anti-distraction strategies. Learning, Media and Technology, 46(3), 355-365. https://doi.org/10.1080/17439884.2021.1896540
2) Aagaard, J. (2022). Taming unruly beings: students, discipline and educational technology. Technology, Pedagogy and Education, 31(2), 159-170. https://doi.org/10.1080/1475939X.2021.1981993
3) Alho, K., Moisala, M., & Salmela-Aro, K. (2022). Effects of Media Multitasking and Video Gaming on Cognitive Functions and Their Neural Bases in Adolescents and Young Adults. European Psychologist, 27(2), 131-140. https://doi.org/10.1027/1016-9040/a000477
4) Anshari, M., Almunawar, M. N., Shahrill, M., Wicaksono, D. K., & Huda, M. (2017). Smartphones usage in the classrooms: Learning aid or interference? Education and Information Technologies, 22(6), 3063-3079. https://doi.org/10.1007/s10639-017-9572-7
5) Benge, J. F., Kiselica, A. M., Aguirre, A., Hilsabeck, R. C., Douglas, M., Paydarfar, D., & Scullin, M. K. (2023). Technology use and subjective cognitive concerns in older adults. Archives of Gerontology and Geriatrics, 106, 104877. https://doi.org/10.1016/j.archger.2022.104877
6) Bonnick, B. (2014). Effects of Classroom Design and Atmosphere towards Affective Reactions and Memory of Content.
7) Cohen, J. (1988). Statistical power analysis Jbr the behavioral. Sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 18-74.
8) Deng, L., Ku, K. Y. L., & Kong, Q. (2019). Examining predictive factors and effects of in-class multitasking with mobile phones. Interactive Technology and Smart Education, 16(1), 49-58. https://doi.org/10.1108/ITSE-08-2018-0056
9) Deng, L., Zhou, Y., & Broadbent, J. (2024). Distraction, multitasking and self-regulation inside university classroom. Education and Information Technologies, 1-23.
10) Dorris, C., Winter, K., O'Hare, L., & Lwoga, E. T. (2021). PROTOCOL: A systematic review of mobile device use in the primary school classroom and its impact on pupil literacy and numeracy attainment. Campbell Systematic Reviews, 17(2), e1155. https://doi.org/10.1002/cl2.1155
11) El Mansouri, M., Strauss, K., Fay, D., & Smith, J. (2024). The cognitive cost of going the extra mile: How striving for improvement relates to cognitive performance. Journal of Applied Psychology. https://doi.org/10.1037/apl0001199
12) Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2), 336. https://doi.org/10.1037/1528-3542.7.2.336
13) Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
14) Fraser, B. J. (1998). Classroom environment instruments: Development, validity and applications. Learning environments research, 1, 7-34. https://doi.org/10.1023/A:1009932514731
15) Gamito, P. S., Morais, D. G., Oliveira, J. G., Brito, R., Rosa, P. J., & de Matos, M. G. (2016). Frequency is not enough: Patterns of use associated with risk of Internet addiction in Portuguese adolescents. Computers in Human Behavior, 58, 471-478. https://doi.org/10.1016/j.chb.2016.01.013
16) Göl, B., Özbek, U., & Horzum, M. B. (2023). Digital distraction levels of university students in emergency remote teaching. Education and Information Technologies, 28(7), 9149-9170. https://doi.org/10.1007/s10639-022-11570-y
17) Gurukkal, R. (2023). Digital Influx in Knowledge Fields. Higher Education for the Future, 10(1), 7-10. https://doi.org/10.1177/23476311231156678
18) Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
19) Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
20) Hanaysha, J. R., Shriedeh, F. B., & In'airat, M. (2023). Impact of classroom environment, teacher competency, information and communication technology resources, and university facilities on student engagement and academic performance. International Journal of Information Management Data Insights, 3(2), 100188. https://doi.org/10.1016/j.jjimei.2023.100188
21) Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
22) Hietajärvi, L., Maksniemi, E., & Salmela-Aro, K. (2022). Digital Engagement and Academic Functioning. European Psychologist, 27(2), 102-115. https://doi.org/10.1027/1016-9040/a000480
23) Hossain, G., & Yeasin, M. (2015). Analysis of cognitive dissonance and overload through ability-demand gap models. IEEE Transactions on Cognitive and Developmental Systems, 9(2), 170-182. https://doi.org/10.1109/TAMD.2015.2450681
24) Ibrahim, R. K., Al Sabbah, S., Al-Jarrah, M., Senior, J., Almomani, J. A., Darwish, A., Albannay, F., & Al Naimat, A. (2024). The mediating effect of digital literacy and self-regulation on the relationship between emotional intelligence and academic stress among university students: a cross-sectional study. BMC Medical Education, 24(1), 1309. https://doi.org/10.1186/s12909-024-06279-0
25) Junaštíková, J. (2024). Self-regulation of learning in the context of modern technology: a review of empirical studies. Interactive Technology and Smart Education, 21(2), 270-291. https://doi.org/10.1108/ITSE-02-2023-0030
26) Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and Satisfaction with Life in college students. Computers in Human Behavior, 31, 343-350. https://doi.org/https://doi.org/10.1016/j.chb.2013.10.049
27) Liao, C.-H., & Wu, J.-Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & education, 190, 104599. https://doi.org/10.1016/j.compedu.2022.104599
28) Liu, Y., & Gu, X. (2020). Media multitasking, attention, and comprehension: a deep investigation into fragmented reading. Educational Technology Research and Development, 68(1), 67-87. https://doi.org/10.1007/s11423-019-09667-2
29) Nunnally, J., & Bernstein, I. (1994). Psychometric Theory, 3r ed., McGraw-Hill, New York, NY.
30) Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: exploring control–value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102(3), 531.
31) Pérez-Juárez, M. Á., González-Ortega, D., & Aguiar-Pérez, J. M. (2023). Digital Distractions from the Point of View of Higher Education Students. Sustainability, 15(7).
32) Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014
33) Raj, K., Segrave, R., Verdéjo-Garcia, A., & Yücel, M. (2023). Cognitive inflexibility and repetitive habitual actions are associated with problematic use of the internet. Addictive Behaviors, 139, 107600. https://doi.org/10.1016/j.addbeh.2022.107600
34) Ringle, C. M. (2015). Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 3. Computational Data Analysis and Numerical Methods VII WCDANM, Portugal. https://scholar.google.com/scholar_lookup?title=SmartPLS%203&publication_year=2015&author=C.M.%20Ringle&author=S.%20Wende&author=J.M.%20Becker
35) Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958. https://doi.org/10.1016/j.chb.2012.12.001
36) Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The Media and Technology Usage and Attitudes Scale: An empirical investigation. Computers in Human Behavior, 29(6), 2501-2511. https://doi.org/10.1016/j.chb.2013.06.006
37) Schmidt, C. R., Schmidt, S. R., & Wilson, K. A. (2022). Individual differences in memory disruption caused by simulated cellphone notifications. Memory, 30(10), 1349-1386. https://doi.org/10.1080/09658211.2022.2120203
38) Schunk, D. H., & Zimmerman, B. J. (2012). Self‐regulation and learning (Vol. 7).
39) Siebers, T., Beyens, I., Pouwels, J. L., & Valkenburg, P. M. (2022). Social Media and Distraction: An Experience Sampling Study among Adolescents. Media Psychology, 25(3), 343-366. https://doi.org/10.1080/15213269.2021.1959350
40) Simanjuntak, E., Nawangsari, N. A. F., & Ardi, R. (2022). Academic cyberslacking: Why do students engage in non-academic internet access during lectures? Psychology research and behavior management, 3257-3273. https://doi.org/10.2147/PRBM.S374745
41) Skulmowski, A., & Xu, K. M. (2022). Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load. Educational Psychology Review, 34(1), 171-196.https://doi.org/10.1007/s10648-021-09624-7
42) Small, G. W., Lee, J., Kaufman, A., Jalil, J., Siddarth, P., Gaddipati, H., Moody, T. D., & Bookheimer, S. Y. (2020). Brain health consequences of digital technology use . Dialogues in Clinical Neuroscience, 22(2), 179-187. https://doi.org/10.31887/DCNS.2020.22.2/gsmall
43) Steinert, S., & Dennis, M. J. (2022). Emotions and Digital Well-Being: on Social Media’s Emotional Affordances. Philosophy & Technology, 35(2), 36. https://doi.org/10.1007/s13347-022-00530-6
44) Sweller, J., Ayres, P., & Kalyuga, S. (2011a). Intrinsic and Extraneous Cognitive Load. In J. Sweller, P. Ayres, & S. Kalyuga (Eds.), Cognitive Load Theory (pp. 57-69). Springer New York. https://doi.org/10.1007/978-1-4419-8126-4_5
45) Sweller, J., Ayres, P., & Kalyuga, S. (2011b). Measuring Cognitive Load. In J. Sweller, P. Ayres, & S. Kalyuga (Eds.), Cognitive Load Theory (pp. 71-85). Springer New York. https://doi.org/10.1007/978-1-4419-8126-4_6
46) Uzun, A. M., & Kilis, S. (2019). Does persistent involvement in media and technology lead to lower academic performance? Evaluating media and technology use in relation to multitasking, self-regulation and academic performance. Computers in Human Behavior, 90, 196-203. https://doi.org/10.1016/j.chb.2018.08.045
47) Vázquez Cano, E., & Sevillano-García, M. L. (2018). Ubiquitous Educational Use of Mobile Digital Devices. A General and Comparative Study in Spanish and Latin America Higher Education. Journal of New Approaches in Educational Research, 7(2), 105-115. https://doi.org/10.7821/naer.2018.7.308
48) Wang, C. (2022). Comprehensively Summarizing What Distracts Students from Online Learning: A Literature Review. Human behavior and emerging technologies, 2022(1), 1483531. https://doi.org/10.1155/2022/1483531
49) Wang, C.-H., Salisbury-Glennon, J. D., Dai, Y., Lee, S., & Dong, J. (2022). Empowering College Students to Decrease Digital Distraction through the Use of Self-Regulated Learning Strategies. Contemporary Educational Technology, 14(4). https://doi.org/10.30935/cedtech/12456
50) Wang, Q., Sun, F., Wang, X., & Gao, Y. (2023). Exploring Undergraduate Students’ Digital Multitasking in Class: An Empirical Study in China. Sustainability, 15(13).
51) Wei, H., & Sun, J. (2024). Examining attentional control deficits in adolescents with test anxiety: An evidential synthesis using self-report, behavioral, and resting-state EEG measures. Acta Psychologica, 246, 104257. https://doi.org/10.1016/j.actpsy.2024.104257
52) Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in Psychology, 8, 605. https://doi.org/10.3389/fpsyg.2017.00605
53) Zhang, L., Wang, B., Xu, Q., & Fu, C. (2023). The role of boredom proneness and self-control in the association between anxiety and smartphone addiction among college students: a multiple mediation model. Frontiers in public health, 11, 1201079. https://doi.org/10.3389/fpubh.2023.1201079
54) Zhao, L. (2023). Social media multitasking and college students' academic performance: A situation–organism–behavior–consequence perspective. Psychology in the Schools, 60(9), 3151-3168. https://doi.org/10.1002/pits.22912
55) Zhou, Y., & Deng, L. (2024). Breaking free from the “digital rabbit hole”: A configurational analysis of in-class smartphone distraction among university students. The Internet and Higher Education, 62, 100949. https://doi.org/10.1016/j.iheduc.2024.100949
56) Zimmerman, B. J. (2000). Chapter 2 - 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. https://doi.org/10.1016/B978-012109890-2/50031-7
57) Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory into practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2
58) Zimmerman, B. J. (2023). Dimensions of academic self-regulation: A conceptual framework for education. In Self-regulation of learning and performance (pp. 3-21). Routledge.
Volume 08 Issue 03 March 2025
There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.
Our Services and Policies
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected.
The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.
International Journal of Multidisciplinary Research and Analysis will publish 12 monthly online issues per year,IJMRA publishes articles as soon as the final copy-edited version is approved. IJMRA publishes articles and review papers of all subjects area.
Open access is a mechanism by which research outputs are distributed online, Hybrid open access journals, contain a mixture of open access articles and closed access articles.
International Journal of Multidisciplinary Research and Analysis initiate a call for research paper for Volume 08 Issue 05 (May 2025).
PUBLICATION DATES:
1) Last Date of Submission : 26 May 2025.
2) Article published within a week.
3) Submit Article : editor@ijmra.in or Online
Why with us
1 : IJMRA only accepts original and high quality research and technical papers.
2 : Paper will publish immediately in current issue after registration.
3 : Authors can download their full papers at any time with digital certificate.
The Editors reserve the right to reject papers without sending them out for review.
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected. The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.