COGNITIVE STRATEGIES IN LEARNING: SELF-REPORTED WORKING MEMORY STRATEGY USE AMONG UNIVERSITY STUDENTS
DOI:
https://doi.org/10.35631/IJEPC.1163080Keywords:
Cognitive Strategies, Higher Education, Self-Reported Learning Strategies, Visuospatial Sketchpad, Working MemoryAbstract
Working memory supports higher-order learning and information processing, particularly attention control, information retention, visual-spatial processing, and verbal rehearsal. Although prior studies have largely examined working memory capacity, training, or performance in controlled cognitive tasks, less is known about how university students report using distinct working memory components as learning strategies in routine academic settings, especially across disciplines and educational levels in Malaysian higher education. Guided by Baddeley’s multicomponent working memory model, this study examined the dominant strategies used by university students and whether self-reported scores differed by gender, education level, and academic discipline. A quantitative cross-sectional survey was conducted with 212 tertiary students from diverse academic fields. Data were collected using a seven-item self-report questionnaire derived from established memory frameworks, reviewed by subject-matter experts, and pilot-tested with 30 participants. The overall scale showed strong internal consistency (Cronbach’s α = .923). The items represented the Central Executive, Visuospatial Sketchpad, and Phonological Loop. By examining these components as self-reported learning strategies in routine university contexts, the study extends the educational application of Baddeley’s framework beyond controlled assessments of working memory capacity. Descriptive and inferential analyses identify patterns of strategy use and demographic differences. The Visuospatial Sketchpad recorded the highest mean (M = 3.95), followed by the Central Executive (M = 3.73) and Phonological Loop (M = 3.55). At the item level, recalling visual appearance had the highest mean (M = 4.00), followed by recalling object location (M = 3.90). No significant differences were found by gender, t = −0.634, p = .527; field of study, t = −0.205, p = .838; or education level, F = 0.462, p = .764. The findings suggest that diagrams, visual mapping, spatial organization, structured task planning, and goal-setting prompts may align with students’ dominant visual-spatial and executive strategies, supporting effective learning in higher education.
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References
Aben, B., Stapert, S., & Blokland, A. (2012). About the distinction between working memory and short-term memory. Frontiers in Psychology, 3(AUG), 1–9. https://doi.org/10.3389/fpsyg.2012.00301
Amin, K. E., & Mohamad, F. S. (2021). Working memory load influence on control of attention among Malaysian undergraduates. Journal of Cognitive Sciences and Human Development. https://doi.org/10.33736/jcshd.2454.2021
Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839. https://doi.org/10.1038/nrn1201
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29. https://doi.org/10.1146/annurev-psych-120710-100422
Champod, A. S., & Petrides, M. (2010). Dissociation within the frontoparietal network in verbal working memory: A parametric functional magnetic resonance imaging study. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.0097-10.2010
Chai, W. J., Abd Hamid, A. I., & Abdullah, J. M. (2018). Working memory from the psychological and neurosciences perspectives: A review. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2018.00401
Cockcroft, K. (2022). Are working memory models WEIRD? Testing models of working memory in a non-WEIRD sample. Neuropsychology. https://doi.org/10.1037/neu0000811
Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review. https://doi.org/10.1007/s10648-013-9246-y
Cowan, N. (2022). Working memory development: A 50-year assessment of research and underlying theories. Cognition, 224, 105075. https://doi.org/10.1016/j.cognition.2022.105075
de Mata, M., Serrano, F., Resing, W., & Vogelaar, B. (2023). Online dynamic testing of reading and writing, executive functioning and reading self-concept in typically developing children and children diagnosed with dyslexia. European Journal of Psychology and Education Research. https://doi.org/10.12973/ejper.6.4.165
Forsberg, A., Adams, E. J., & Cowan, N. (2021). The role of working memory in long-term learning: Implications for childhood development. In B. H. Ross (Ed.), Psychology of Learning and Motivation (Vol. 74, pp. 1-45). Academic Press. https://doi.org/10.1016/bs.plm.2021.02.00
Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Psychology Press.
Guler, K., & Aydin, A. (2022). Comparing the working memory capacity with cognitive flexibility, cognitive emotion regulation, and learning styles of university students: A domain general view. Dementia and Geriatric Cognitive Disorders Extra. https://doi.org/10.1159/000526226
Greenberg, K., Zheng, R., Gardner, M., & Orr, M. (2021). Individual differences in visuospatial working memory capacity influence the modality effect. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12519
Henry, L. A., & Bettenay, C. (2010). The assessment of executive functioning in children. Child and Adolescent Mental Health. https://doi.org/10.1111/j.1475-3588.2010.00557.x
Holmes, J., Guy, J., Kievit, R. A., Bryant, A., Mareva, S., & Gathercole, S. E. (2020). Cognitive dimensions of learning in children with problems in attention, learning, and memory. Journal of Educational Psychology. https://doi.org/10.1037/edu0000644
Karim, A. A., Din, R., Razak, N. A., Abdullah, M. Y., & Hussin, S. (2010). Mapping the landscape of students’ learning information skills and constructing knowledge in Malaysian higher education. Proceedings of the International Conference on Education and Educational Technologies.
Kim, H., Smolker, H. R., Smith, L. L., Banich, M. T., & Lewis-Peacock, J. A. (2020). Changes to information in working memory depend on distinct removal operations. Nature Communications. https://doi.org/10.1038/s41467-020-20085-4
Kofler, M. J., Soto, E. F., Fosco, W. D., Irwin, L. N., Wells, E. L., & Sarver, D. E. (2020). Working memory and information processing in ADHD: Evidence for directionality of effects. Neuropsychology. https://doi.org/10.1037/neu0000598
Kolling, T., Graf, F., & Knopf, M. (2016). Cross-cultural perspectives on declarative and nondeclarative memory from infancy to early childhood. Child Development Perspectives. https://doi.org/10.1111/cdep.12158
Kouvatsou, Z., Masoura, E., Kiosseoglou, G., & Kimiskidis, V. K. (2022). Evaluating the relationship between working memory and information processing speed in multiple sclerosis. Applied Neuropsychology. https://doi.org/10.1080/23279095.2020.1804911
Linares, R., & Pelegrina, S. (2023). The relationship between working memory updating components and reading comprehension. Cognitive Processing. https://doi.org/10.1007/s10339-023-01127-3
Maezawa, T., & Kawahara, J. I. (2023). Processing symmetry between visual and auditory spatial representations in updating working memory. Quarterly Journal of Experimental Psychology. https://doi.org/10.1177/17470218221103253
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Müller, F. A., & Wulf, T. (2024). Differences in learning effectiveness across management learning environments: A cognitive load theory perspective. Journal of Management Education. https://doi.org/10.1177/10525629231200206
Oberauer, K. (2019). Working memory and attention: A conceptual analysis and review. Journal of Cognition. https://doi.org/10.5334/joc.58
Olszewska, J. M., Reuter-Lorenz, P. A., Munier, E., & Bendler, S. A. (2015). Misremembering what you see or hear: Dissociable effects of modality on short- and long-term false recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0000115
Rostami, M., & Mehdi Abadi, P. (2023). The impact of doing assignments with chatbots on the students’ working memory. Health Nexus, 1(1), 10. https://doi.org/10.61838/hn.1.1.10
Ryan, T. J., de San Luis, C. O., Pezzoli, M., & Sen, S. (2021). Engram cell connectivity: An evolving substrate for information storage. Current Opinion in Neurobiology. https://doi.org/10.1016/j.conb.2021.01.006
Shamila, M. S., Norizan, A., Azman, M. I., Nordiana, M. N., & Ismail, A. M. (2023). Information literacy skills among higher education institution students in Malaysia. In Innovative Educational Practices in Higher Education (pp. 111–123). Springer. https://doi.org/10.1007/978-981-99-0522-5_8
Soemer, A. (2016). The multicomponent working memory model, attention, and long-term memory in multimedia learning: A comment on Schweppe and Rummer (2014). Educational Psychology Review. https://doi.org/10.1007/s10648-015-9303-9
Sutterer, D. W., Foster, J. J., Serences, J. T., Vogel, E. K., & Awh, E. (2019). Alpha-band oscillations track the retrieval of precise spatial representations from long-term memory. Journal of Neurophysiology. https://doi.org/10.1152/jn.00268.2019
Swanson, H. L., Moran, A., Lussier, C., & Fung, W. (2014). The effect of explicit and direct generative strategy training and working memory on word problem-solving accuracy in children at risk for math difficulties. Learning Disability Quarterly. https://doi.org/10.1177/0731948713507264
Swanson, H. L., Zheng, X., & Jerman, O. (2009). Working memory, short-term memory, and reading disabilities: A selective meta-analysis of the literature. Journal of Learning Disabilities, 42(3), 260–287. https://doi.org/10.1177/0022219409331958
Zainal Abidin, N. A., Fathi, M. S., Md Daud, M. Y., & Baharum, H. I. (2015). A preliminary study on the learning perspective at Malaysia’s government technical department. Applied Mechanics and Materials, 773–774, 809–814.
