The Dual Impact of Smartphone Usage on Short-Term Memory, Attention, and Academic Performance in Digital Learning Environments
DOI:
https://doi.org/10.61194/psychology.v2i3.503Keywords:
Smartphone use, digital learning, cognitive engagement, memory retention, education technology, mobile learning, student performanceAbstract
Smartphones have become an integral part of modern education, offering both opportunities and challenges in learning environments. This study explores the impact of smartphone usage on cognitive processes, specifically short-term memory and attention, through a systematic literature review. Utilizing databases such as Scopus, Google Scholar, and PubMed, relevant studies were analyzed to assess the benefits and drawbacks of smartphone integration in education. The findings reveal that structured smartphone use, including gamified learning tools and reinforcement models, enhances engagement and knowledge retention. However, unregulated smartphone use leads to cognitive overload, distractions, and reduced academic performance. The study emphasizes the importance of policy reforms, teacher training, and digital literacy programs to maximize the benefits of mobile technology in education while mitigating its negative effects. Future research should focus on longitudinal studies and cross-cultural comparisons to refine best practices for smartphone-assisted learning. The results underscore the need for balanced technology integration to optimize learning outcomes in an increasingly digitalized academic landscape.
References
Anda‐Duran, I. D., Sunderaraman, P., Searls, E., Moukaled, S., Jin, X., Popp, Z., Karjadi, C., Hwang, P. H., Ding, H., Devine, S., Shih, L. C., Low, S., Lin, H., Kolachalama, V. B., Bazzano, L., Libon, D. J., & Au, R. (2024). Comparing Cognitive Tests and Smartphone‐Based Assessment in 2 US Community‐Based Cohorts. Journal of the American Heart Association, 13(2). https://doi.org/10.1161/jaha.123.032733 DOI: https://doi.org/10.1161/JAHA.123.032733
Ayoubi, S., Limam, N., Salahuddin, M. A., Shahriar, N., Boutaba, R., Estrada-Solano, F., & Caicedo, O. M. (2018). Machine Learning for Cognitive Network Management. IEEE Communications Magazine, 56(1), 158–165. https://doi.org/10.1109/MCOM.2018.1700560 DOI: https://doi.org/10.1109/MCOM.2018.1700560
Canale, N., Vieno, A., Doro, M., Mineo, E. R., Marino, C., & Billieux, J. (2019). Emotion-Related Impulsivity Moderates the Cognitive Interference Effect of Smartphone Availability on Working Memory. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-54911-7 DOI: https://doi.org/10.1038/s41598-019-54911-7
Capelini, C. M., Silva, T. D. d., Tonks, J., Watson, S., Alvarez, M. P. B., Menezes, L. D. C. de, Fávero, F. M., Caromano, F. A., Massetti, T., & Monteiro, C. B. de M. (2017). Improvements in Motor Tasks Through the Use of Smartphone Technology for Individuals With Duchenne Muscular Dystrophy. Neuropsychiatric Disease and Treatment, Volume 13, 2209–2217. https://doi.org/10.2147/ndt.s125466 DOI: https://doi.org/10.2147/NDT.S125466
Cerino, E., Katz, M. J., Wang, C., Qin, J., Gao, Q., Hyun, J., Hakun, J. G., Roque, N., Derby, C. A., Lipton, R. B., & Sliwinski, M. J. (2021). Variability in Cognitive Performance on Mobile Devices Is Sensitive to Mild Cognitive Impairment: Results From the Einstein Aging Study. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.758031 DOI: https://doi.org/10.3389/fdgth.2021.758031
Hjertø, K. B., Paulsen, J. M., & Tihverainen, S. P. (2014). Social-cognitive outcomes of teachers’ engagement in learning communities. Journal of Educational Administration, 52(6), 775–791. https://doi.org/10.1108/JEA-07-2013-0074 DOI: https://doi.org/10.1108/JEA-07-2013-0074
Hyun, J., Sliwinski, M. J., & Smyth, J. M. (2018). Waking Up on the Wrong Side of the Bed: The Effects of Stress Anticipation on Working Memory in Daily Life. The Journals of Gerontology Series B, 74(1), 38–46. https://doi.org/10.1093/geronb/gby042 DOI: https://doi.org/10.1093/geronb/gby042
Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189–199. https://doi.org/10.1016/j.chb.2016.12.049 DOI: https://doi.org/10.1016/j.chb.2016.12.049
Iqbal, J., Asghar, M. Z., Ashraf, M. A., & Yi, X. (2022). The Impacts of Emotional Intelligence on Students’ Study Habits in Blended Learning Environments: The Mediating Role of Cognitive Engagement during COVID-19. Behavioral Sciences, 12(1). https://doi.org/10.3390/BS12010014 DOI: https://doi.org/10.3390/bs12010014
Jang, J., Parrila, R., & Inoue, T. (2023). Relations of Vocabulary and Cognitive Skills With Reading Performance of North Korean Students in South Korean Schools. Journal of Psycholinguistic Research, 52(1), 241–259. https://doi.org/10.1007/s10936-022-09855-x DOI: https://doi.org/10.1007/s10936-022-09855-x
Jasmine Lizy, P., & Chenthalir Indra, N. (2023). Outlier detection based energy efficient and reliable routing protocol using deep learning algorithm. Cognitive Computation and Systems, 5(2), 138–152. https://doi.org/10.1049/ccs2.12083 DOI: https://doi.org/10.1049/ccs2.12083
Katsarou, K., Yu, G., & Beierle, F. (2022). WhatsNextApp: LSTM-Based Next-App Prediction With App Usage Sequences. Ieee Access, 10, 18233–18247. https://doi.org/10.1109/access.2022.3150874 DOI: https://doi.org/10.1109/ACCESS.2022.3150874
Könen, T., Dirk, J., & Schmiedek, F. (2014). Cognitive Benefits of Last Night’s Sleep: Daily Variations in Children’s Sleep Behavior Are Related to Working Memory Fluctuations. Journal of Child Psychology and Psychiatry, 56(2), 171–182. https://doi.org/10.1111/jcpp.12296 DOI: https://doi.org/10.1111/jcpp.12296
McNab, F., & Dolan, R. J. (2014). Dissociating Distractor-Filtering at Encoding and During Maintenance. Journal of Experimental Psychology Human Perception & Performance, 40(3), 960–967. https://doi.org/10.1037/a0036013 DOI: https://doi.org/10.1037/a0036013
Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. U. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00261-x DOI: https://doi.org/10.1186/s40561-023-00261-x
Otto, L., Thomas, F., Glogger, I., & Vreese, C. H. d. (2021). Linking Media Content and Survey Data in a Dynamic and Digital Media Environment – Mobile Longitudinal Linkage Analysis. Digital Journalism, 10(1), 200–215. https://doi.org/10.1080/21670811.2021.1890169 DOI: https://doi.org/10.1080/21670811.2021.1890169
Pereira, C. V. F., Oliveira, E. M. d., & Souza, A. D. d. (2024). Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature. Sensors, 24(19), 6322. https://doi.org/10.3390/s24196322 DOI: https://doi.org/10.3390/s24196322
Rodríguez, F. M. M., Lozano, J. M. G., Mingorance, P. L., & Pérez-Mármol, J. M. (2020). Influence of smartphone use on emotional, cognitive and educational dimensions in university students. Sustainability (Switzerland), 12(16). https://doi.org/10.3390/su12166646 DOI: https://doi.org/10.3390/su12166646
Schneider, J., Klüner, A., & Zielinski, O. (2023). Towards Digital Twins of the Oceans: The Potential of Machine Learning for Monitoring the Impacts of Offshore Wind Farms on Marine Environments. Sensors, 23(10). https://doi.org/10.3390/s23104581 DOI: https://doi.org/10.3390/s23104581
Smith, A. J., Shoji, K., Griffin, B. J., Sippel, L. M., Dworkin, E. R., Wright, H. M., Morrow, E., Locke, A., Love, T. M., Harris, J. I., Kaniasty, K., Langenecker, S. A., & Benight, C. C. (2022). Social cognitive mechanisms in healthcare worker resilience across time during the pandemic. Social Psychiatry and Psychiatric Epidemiology, 57(7), 1457–1468. https://doi.org/10.1007/s00127-022-02247-5 DOI: https://doi.org/10.1007/s00127-022-02247-5
Smith, K. L., Tsai, H., Lim, D., Wang, C., Nunes, R., Wilkinson, M., Sheng, J. Y., Couzi, R., Fetting, J. H., Riley, C., Wolff, A. C., Santa‐Maria, C. A., Papathakis, K., Collins-Chase, L., Hilton, C., Thorner, E., Montanari, A., Ikejiani, D. Z., Snyder, C., & Stearns, V. (2023). Feasibility of Symptom Monitoring During the First Year of Endocrine Therapy for Early Breast Cancer Using Patient-Reported Outcomes Collected via Smartphone App. Jco Oncology Practice, 19(11), 981–989. https://doi.org/10.1200/op.23.00038 DOI: https://doi.org/10.1200/OP.23.00038
Tanil, C. T., & Yong, M. H. (2020). Mobile Phones: The Effect of Its Presence on Learning and Memory. Plos One, 15(8), e0219233. https://doi.org/10.1371/journal.pone.0219233 DOI: https://doi.org/10.1371/journal.pone.0219233
Thompson, L. I., Harrington, K., Roque, N., Strenger, J., Correia, S., Jones, R. N., Salloway, S., & Sliwinski, M. J. (2022). A Highly Feasible, Reliable, and Fully Remote Protocol for Mobile App‐based Cognitive Assessment in Cognitively Healthy Older Adults. Alzheimer S & Dementia Diagnosis Assessment & Disease Monitoring, 14(1). https://doi.org/10.1002/dad2.12283 DOI: https://doi.org/10.1002/dad2.12283
Troseth, G. L., Strouse, G. A., & Johnson, C. E. R. (2017). Early Digital Literacy: Learning to Watch, Watching to Learn. In Cognitive Development in Digital Contexts (pp. 29–51). https://doi.org/10.1016/B978-0-12-809481-5.00002-X. DOI: https://doi.org/10.1016/B978-0-12-809481-5.00002-X
Wang, X., Bakulski, K. M., Paulson, H. L., Albin, R. L., & Park, S. K. (2023). Associations of healthy lifestyle and socioeconomic status with cognitive function in U.S. older adults. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-34648-0 DOI: https://doi.org/10.1038/s41598-023-34648-0
Willett, C. L., & Rottman, B. M. (2021). The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach. Cognitive Science, 45(7). https://doi.org/10.1111/cogs.12985 DOI: https://doi.org/10.1111/cogs.12985
Wiradhany, W., Pócs, A., & Baumgartner, S. E. (2024). Are Social Media Notifications Distracting? Experimental Psychology (Formerly Zeitschrift Für Experimentelle Psychologie), 71(4), 189–201. https://doi.org/10.1027/1618-3169/a000625 DOI: https://doi.org/10.1027/1618-3169/a000625
Yadav, A., Pasupa, K., Loo, C. K., & Liu, X. (2024). Optimizing Echo State Networks for Continuous Gesture Recognition in Mobile Devices: A Comparative Study. Heliyon, 10(5), e27108. https://doi.org/10.1016/j.heliyon.2024.e27108 DOI: https://doi.org/10.1016/j.heliyon.2024.e27108
Yarahmadi, R., & Soleimani-Alyar, S. (2021). THE PERFORMANCE ANALYSIS OF HEALTH, SAFETY, ENVIRONMENT, AND ENERGY INTEGRATED MANAGEMENT SYSTEM (HSEE-IMS) USING FUZZY COGNITIVE MAPPING METHOD. Proceedings on Engineering Sciences, 3(4), 441–452. https://doi.org/10.24874/PES03.04.008 DOI: https://doi.org/10.24874/PES03.04.008
Yuan, M., Chen, J., Han, Y., Wei, X., Ye, Z., Zhang, L., Hong, Y., & Fang, Y. (2018). Associations Between Modifiable Lifestyle Factors and Multidimensional Cognitive Health Among Community-Dwelling Old Adults: Stratified by Educational Level. International Psychogeriatrics, 30(10), 1465–1476. https://doi.org/10.1017/s1041610217003076 DOI: https://doi.org/10.1017/S1041610217003076