Trends and Effects of Psychological and Cognitive Load in Education
Article Sidebar
Main Article Content
Abstract
Background and Aim: The cognitive load experienced by students during learning can lead to mental fatigue and significant psychological disturbances, often resulting in negative emotional responses. This study aims to identify trends and examine influential factors in psychological aspects and cognitive load within the educational context, thereby advancing the understanding of educational psychology and cognitive load theory.
Materials and Methods: Utilizing a mixed-methods approach, the research integrates bibliometric analysis and a systematic literature review, allowing a comprehensive evaluation of scholarly trends. Research data were sourced from the Scopus database with the keywords "Psychological" AND "Cognitive Load" AND "Education" in article titles, abstracts, and keywords, ensuring alignment with relevant literature.
Results: Findings show a fluctuating trend in annual publication citations, marked by phases of notable increases and declines. These patterns are shaped by topical relevance, research quality, and shifts in the academic landscape and technology for information access. The United States leads in publication contributions, comprising 27.4% of total articles, with significant use of terms like cognitive load, learning systems, executive function, and motivation in recent research, while terms like humans, cognition, and education remain consistently emphasized. The insights suggest that innovative learning media, while enhancing outcomes, introduce complexities in managing cognitive load.
Conclusion: This study underscores the need for precise cognitive load measurement in developing effective interventions, emphasizing the management of students' emotions as a critical component in online learning and technology acceptance. These findings inform educational practices and policies by highlighting strategies that support emotional well-being and foster effective, student-centred learning environments, laying the groundwork for future research on enhancing educational practices through cognitive and emotional insights.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the Journal of Education and Learning Reviews is retained by the author(s) under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Ajmal, M., & Ahmad, S. (2019). Exploration of Anxiety Factors among Students of Distance Learning: A Case Study of Allama Iqbal Open University. Bulletin of Education and Research, 41(2), 67–78.
Andersen, M. S., & Makransky, G. (2021). The Validation and Further Development of the Multidimensional Cognitive Load Scale for Physical and Online Lectures (MCLS-POL). Frontiers in Psychology, 12(3), 1–11. https://doi.org/10.3389/fpsyg.2021.642084
Athilingam, P., Osorio, R. E., Kaplan, H., Oliver, D., O’neachtain, T., & Rogal, P. J. (2016). Embedding Patient Education in Mobile Platform for Patients With Heart Failure. CIN: Computers, Informatics, Nursing, 34(2), 92–98. https://doi.org/10.1097/cin.0000000000000216
Atiomo, W., Ennab, F., Stanley, A., & Ezimokhai, M. (2024). Evaluating an obstetrics and gynecology teaching program for medical students incorporating simulation-based education underpinned by cognitive load theory. Frontiers in Medicine, 11(3), 1–10. https://doi.org/10.3389/fmed.2024.1304417
Avgerinou, V. A., & Tolmie, A. (2020). Inhibition and cognitive load in fractions and decimals. British Journal of Educational Psychology, 90(1), 240–256. https://doi.org/10.1111/bjep.12321
Banikowski, A. K., & Mehring, T. A. (1999). Strategies to enhance memory based on brain research. Focus on Exceptional Children, 32(2), 1–16.
Bidzan-Bluma, I., Jurek, P., & Lipowska, M. (2024). Cognitive Functions in Pre-Adolescent Children Involved in Gymnastics and Soccer. Advances in Cognitive Psychology, 20(2), 149–157. https://doi.org/10.5709/acp-0424-8
Boekaerts, M., & Pekrun, R. (2015). Emotions and emotion regulation in academic settings. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 90–104). Routledge. https://doi.org/10.4324/9781315688244-15
Brunzini, A., Papetti, A., Messi, D., & Germani, M. (2022). A comprehensive method to design and assess mixed reality simulations. Virtual Reality, 26(4), 1257–1275. https://doi.org/10.1007/s10055-022-00632-8
Cai, L., & Yang, G. (2020). Development and practice of virtual experiment platform based on Blender and HTML5—Taking computer assembly and maintenance as an example. Journal of Physics: Conference Series, 1601(3), 032034. https://doi.org/10.1088/1742-6596/1601/3/032034
Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology. British Journal of Educational Psychology, 62, 233–246.
Chen, Y., Zhou, H., Zhou, Y., & Zhou, F. (2020). Prevalence of self-reported depression and anxiety among pediatric medical staff members during the COVID-19 outbreak in Guiyang, China. Psychiatry Research, 288, 113005.
Choi, Y., & Lee, H. (2022). Psychometric Properties for Multidimensional Cognitive Load Scale in an E-Learning Environment. International Journal of Environmental Research and Public Health, 19(10), 1–12. https://doi.org/10.3390/ijerph19105822
Costa, C., Alvelos, H., & Teixeir, L. (2019). Investigating the Use and Acceptance of Technologies by Professors in a. International Tional Journal of Online Pedagogy and Course Design, 9(2), 1–20.
Creswell, J. W. (1999). Mixed-method research: Introduction and application. In G. J. Cizek (Ed.), Handbook of educational policy (pp. 455–472). Academic Press.
Dede, C., Korte, S., Nelson, R., & J, V. D. (2005). Transforming learning for the 21st century: An economic imperative. Common Knowledge, 399, 1–66.
DeLeeuw, K. E., & Mayer, R. E. (2008). A Comparison of Three Measures of Cognitive Load: Evidence for Separable Measures of Intrinsic, Extraneous, and Germane Load. Journal of Educational Psychology, 100(1), 223–234. https://doi.org/10.1037/0022-0663.100.1.223
Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2023). Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review. International Journal of Environmental Research and Public Health, 20(1),173. https://doi.org/10.3390/ijerph20010173
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(May), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Durning, S., Artino, A. R., Pangaro, L., van der Vleuten, C. P., & Schuwirth, L. (2011). Context and clinical reasoning: Understanding the perspective of the expert’s voice. Medical Education, 45(9), 927–938. https://doi.org/10.1111/j.1365-2923.2011.04053.x
Fraser, K., Huffman, J., Ma, I., Sobczak, M., McIlwrick, J., Wright, B., & McLaughlin, K. (2014). The emotional and cognitive impact of unexpected simulated patient death. Chest, 145(5), 958–963. https://doi.org/10.1378/chest.13-0987
Fraser, K., Ma, I., Teteris, E., Baxter, H., Wright, B., & McLaughlin, K. (2012). Emotion, cognitive load, and learning outcomes during simulation training. Medical Education, 46(11), 1055–1062. https://doi.org/10.1111/j.1365-2923.2012.04355.x
Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material. In Psychology of learning and motivation (pp. 183–225). Academic Press.
Güth, F., & van Vorst, H. (2024). To choose or not to choose? Effects of choice in authentic context-based learning environments. In European Journal of Psychology of Education (Issue 0123456789). Springer Netherlands. https://doi.org/10.1007/s10212-024-00798-6
Haji, F. A., Khan, R., Regehr, G., Drake, J., de Ribaupierre, S., & Dubrowski, A. (2015). Measuring cognitive load during simulation-based psychomotor skills training: sensitivity of secondary-task performance and subjective ratings. Advances in Health Sciences Education, 20(5), 1237–1253. https://doi.org/10.1007/s10459-015-9599-8
Haji, F. A., Rojas, D., Childs, R., de Ribaupierre, S., & Dubrowski, A. (2015). Measuring cognitive load: Performance, mental effort, and simulation task complexity. Medical Education, 49(8), 815–827. https://doi.org/10.1111/medu.12773
Han, E., Nowak, K. L., & Bailenson, J. N. (2022). Prerequisites for Learning in Networked Immersive Virtual Reality. Technology, Mind, and Behavior, 3(4). https://doi.org/10.1037/tmb0000094
Holzinger, A., Kickmeier-Rust, M. D., Wassertheurer, S., & Hessinger, M. (2009). Learning performance with interactive simulations in medical education: Lessons learned from results of learning complex physiological models with the HAEMOdynamics SIMulator. Computers and Education, 52(2), 292–301. https://doi.org/10.1016/j.compedu.2008.08.008
Huang, Z., Wei, P., & Tao, X. (2020). Study on the effect of video learning sequence on eye movement behavior and learning outcome of learners. Proceedings - 2020 International Conference on Information Science and Education, ICISE-IE 2020, 61906051, 659–663. https://doi.org/10.1109/ICISE51755.2020.00148
Husky, M. M., Kovess-Masfety, V., & Swendsen, J. D. (2020). Stress and anxiety among university students in France during the COVID-19 mandatory confinement. Comprehensive Psychiatry, 102, 152191.
Hwang, M. Y., Hong, J. C., Hsu, T. F., & Chen, Y. J. (2011). The relation between students’ anxiety and interest in playing an online game. 2011 IEEE International Games Innovation Conference, IGIC 2011, 37–39. https://doi.org/10.1109/IGIC.2011.6114852
Ji, M., Luo, C., Ren, J., & Yang, Y. (2023). A comparative study of the cognitive load of basic-level category, superordinate category, and subordinate category. Psychological Research, 87(7), 2192–2203. https://doi.org/10.1007/s00426-023-01799-8
Joksimovic, S., Gasevic, D., Kovanovic, V., Adesope, O., & Hatala, M. (2014). Psychological characteristics in cognitive presence of communities of inquiry: A linguistic analysis of online discussions. Internet and Higher Education, 22, 1–10. https://doi.org/10.1016/j.iheduc.2014.03.001
Kadir, M. S., Yeung, A. S., Ryan, R. M., Forbes, A., & Diallo, T. M. O. (2020). Effects of a Dual-Approach Instruction on Students’ Science Achievement and Motivation. Educational Psychology Review, 32(2), 571–602. https://doi.org/10.1007/s10648-018-9449-3
Khalil, M. K., Mansour, M. M., & Wilhite, D. R. (2010). Evaluation of cognitive loads imposed by traditional paper-based and innovative computer-based instructional strategies. Journal of Veterinary Medical Education, 37(4), 353–357. https://doi.org/10.3138/jvme.37.4.353
Khalil, M. K., Paas, F., Johnson, T. E., & Payer, A. F. (2005). Design of interactive and dynamic anatomical visualizations: The implication of cognitive load theory. Anatomical Record - Part B New Anatomist, 286(1), 15–20. https://doi.org/10.1002/ar.b.20078
Klein, M., Otto, B., Fischer, M. R., & Stark, R. (2019). Fostering medical students’ clinical reasoning by learning from errors in clinical case vignettes: effects and conditions of additional prompting procedures to foster self-explanations. Advances in Health Sciences Education, 24(2), 331–351. https://doi.org/10.1007/s10459-018-09870-5
Lee, H., Park, S., Kim, E. H., Seo, J., Lim, H., & Lee, J. (2024). Investigating the Effects of a Real-time Student Monitoring Interface on Instructors’ Monitoring Practices in Online Teaching. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3613904.3642845
Makransky, G., & Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): A Theoretical Research-Based Model of Learning in Immersive Virtual Reality. Educational Psychology Review, 33(3), 937–958. https://doi.org/10.1007/s10648-020-09586-2
Mayer, R. E. (2008). Learning and instruction (2nd ed.). Pearson.
Mega, C., Ronconi, L., & Beni, R. De. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121.
Mohan, D., Rosengart, M. R., Fischhoff, B., Angus, D. C., Farris, C., Yealy, D. M., Wallace, D. J., & Barnato, A. E. (2016). Testing a videogame intervention to recalibrate physician heuristics in trauma triage: Study protocol for a randomized controlled trial. BMC Emergency Medicine, 16(1), 1–10. https://doi.org/10.1186/s12873-016-0108-z
Murata, Y., & Suzuki, S. (2015). Artifact robust estimation of cognitive load by measuring cerebral blood flow. Proceedings - 2015 8th International Conference on Human System Interaction, HSI 2015, 302–308. https://doi.org/10.1109/HSI.2015.7170684
Murphy, G., Groeger, J. A., & Greene, C. M. (2016). Twenty years of load theory—Where are we now, and where should we go next? Current Directions in Psychological Science, 19(3), 143–148. https://doi.org/10.1177/0963721410370295
Naismith, L. M., Cheung, J. J. H., Ringsted, C., & Cavalcanti, R. B. (2015). Limitations of subjective cognitive load measures in simulation-based procedural training. Medical Education, 49(8), 805–814. https://doi.org/10.1111/medu.12732
Paas, F. P., Renkl, A. R., & Sweller, J. S. (2016). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist: A Special Issue of Educational Psychologist: Volume 38, 38(June 2010), 1–4. https://doi.org/10.4324/9780203764770-1
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., Mcdonald, S., … Mckenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. The BMJ, 372. https://doi.org/10.1136/bmj.n160
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2020). Academic Emotions in Students’ Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research. Emotions in Education, 37, 91–106. https://doi.org/10.4324/9781410608628-4
Rana, J., & Burgin, S. (2017). Teaching & learning tips 2: cognitive load theory. International journal of dermatology. International Journal of Dermatology, 56(12), 1438–1441.
Rashid, A., Yasmeen, R., Ahmed, R., & Jawed, K. (2022). Factors leading to the academic failure of undergraduate medical students-Predict early to prevent. Pakistan Journal of Medical Sciences, 38(8), 2071–2075. https://doi.org/10.12669/pjms.38.8.5951
Russ, S. J., Morrison, I., Bell, C., Morse, J. C., MacKenzie, R. K., & Johnston, M. K. (2018). Relationship between acute stress and clinical performance in medical students: A pilot simulation study. BMJ Simulation and Technology Enhanced Learning, 4(4), 171–178. https://doi.org/10.1136/bmjstel-2017-000276
Schrader, C., & Net, U. (2018). The perception of control as a predictor of emotional trends during gameplay. Learning and Instruction. Learning and Instruction, 54, 62–72.
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
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
Sweller, J., Ayres, P., & Kalyuga, S. (2014). Cognitive load theory. Springer.
Sweller, J., Merrienboer, J. J. G. van, & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design John Sweller,1,4 Jeroen J. G. van Merrienboer,2 and Fred G. W. C. Paas3 Cognitive. Entomologia Experimentalis et Applicata, 3(10), 251–295. https://doi.org/10.1023/A
Szulewski, A., Gegenfurtner, A., Howes, D. W., Sivilotti, M. L. A., & van Merriënboer, J. J. G. (2017). Measuring physician cognitive load: validity evidence for a physiologic and a psychometric tool. Advances in Health Sciences Education, 22(4), 951–968. https://doi.org/10.1007/s10459-016-9725-2
Tang, Q., Wang, Y., Liu, H., Liu, Q., & Jiang, S. (2022). Experiencing an art education program through immersive virtual reality or iPad: Examining the mediating effects of sense of presence and extraneous cognitive load on enjoyment, attention, and retention. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.957037
Toma, L., & Vahrenhold, J. (2018). Self-efficacy, cognitive load, and emotional reactions in collaborative algorithms labs - A case study. ICER 2018 - Proceedings of the 2018 ACM Conference on International Computing Education Research, 1–10. https://doi.org/10.1145/3230977.3230980
Tzafilkou, K., Perifanou, M., & Economides, A. A. (2021). Negative emotions, cognitive load, acceptance, and self-perceived learning outcome in emergency remote education during COVID-19. Education and Information Technologies, 26(6), 7497–7521. https://doi.org/10.1007/s10639-021-10604-1
Ureña, N., Fernández, N., Cárdenas, D., Madinabeitia, I., & Alarcón, F. (2020). Acute effect of cognitive compromise during physical exercise on self-regulation in early childhood education. International Journal of Environmental Research and Public Health, 17(24), 1–18. https://doi.org/10.3390/ijerph17249325
Wang, Y., Gareth Gaskell, M., & Gennari, S. P. (2024). Influences of learned verbal labels and sleep on temporal event memory. Journal of Memory and Language, 138(September 2023), 104529. https://doi.org/10.1016/j.jml.2024.104529
Werner, A., Holderried, F., Schäffeler, N., Weyrich, P., Riessen, R., Zipfel, S., & Celebi, N. (2013). Communication training for advanced medical students improves information recall of medical laypersons in simulated informed consent talks - A randomized controlled trial. BMC Medical Education, 13(1). https://doi.org/10.1186/1472-6920-13-15
Yan, D., & Zhao, T. (2019). Emotions, engagement, and self‐perceived achievement in a small private online course. Journal of Computer Assisted Learning, 36, 449–457.
Young, J. Q., van Dijk, S. M., O’Sullivan, P. S., Custers, E. J., Irby, D. M., & Ten Cate, O. (2016). Influence of learner knowledge and case complexity on handover accuracy and cognitive load: results from a simulation study. Medical Education, 50(9), 969–978. https://doi.org/10.1111/medu.13107