Cognitive Load Theory: Implications for Instructional Design in Digital Classrooms
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Cognitive Load Theory, Digital Classrooms, Educational Technology, Instructional Design, Learning Efficiency
Abstract
The rapid integration of digital tools in education has transformed classroom environments, creating new opportunities and challenges for instructional design. One key area of focus is the management of cognitive load, which refers to the mental effort required to process information during learning. Cognitive Load Theory (CLT) offers insights into how instructional materials can be optimized to improve learning outcomes. In digital classrooms, the effective design of instructional content becomes even more critical due to the increased multimedia elements and potential for cognitive overload. This study aims to explore the implications of Cognitive Load Theory (CLT) for instructional design in digital classrooms. It examines how digital tools, such as multimedia content and interactive activities, impact learners’ cognitive load and suggests strategies for reducing extraneous cognitive load to enhance learning efficiency and effectiveness. A mixed-methods approach was used, combining quantitative surveys to assess students’ cognitive load during digital learning activities and qualitative interviews with instructors to understand their perspectives on instructional design challenges. The study was conducted across several digital learning environments in higher education. The findings indicate that digital learning environments often lead to high cognitive load, particularly when multimedia content is poorly integrated. However, using principles from CLT, such as segmenting information and reducing unnecessary complexity, can significantly lower cognitive load and improve student learning outcomes. Both students and instructors reported that well-designed digital content led to better engagement and more efficient learning. The study concludes that applying Cognitive Load Theory to instructional design in digital classrooms can enhance learning by minimizing cognitive overload. Educators should be mindful of cognitive load when creating digital learning experiences to improve student performance and engagement.
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References
Adinda, D. (2020). Teaching and instructional design approaches to enhance students’ self-directed learning in blended learning environments. Electronic Journal of E-Learning, 18(2), 162–174. https://doi.org/10.34190/EJEL.20.18.2.005
Ayres, P. (2020). Something old, something new from cognitive load theory. Computers in Human Behavior, 113(Query date: 2024-11-30 12:05:35). https://doi.org/10.1016/j.chb.2020.106503
Bolkan, S. (2021). Predicting 4-Year Graduation: Using Social Cognitive Career Theory to Model the Impact of Prescriptive Advising, Unit Load, and Students’ Self-Efficacy. Journal of College Student Retention: Research, Theory and Practice, 22(4), 655–675. https://doi.org/10.1177/1521025118783485
Bruin, A. B. H. de. (2020). Synthesizing Cognitive Load and Self-regulation Theory: A Theoretical Framework and Research Agenda. Educational Psychology Review, 32(4), 903–915. https://doi.org/10.1007/s10648-020-09576-4
Castro-Alonso, J. C. (2020a). Latest trends to optimize computer-based learning: Guidelines from cognitive load theory. Computers in Human Behavior, 112(Query date: 2024-11-30 12:05:35). https://doi.org/10.1016/j.chb.2020.106458
Castro-Alonso, J. C. (2020b). The Modality Effect of Cognitive Load Theory. Advances in Intelligent Systems and Computing, 963(Query date: 2024-11-30 12:05:35), 75–84. https://doi.org/10.1007/978-3-030-20135-7_7
Chan, P. (2021). Virtual chemical laboratories: A systematic literature review of research, technologies and instructional design. Computers and Education Open, 2(Query date: 2024-11-30 19:09:04). https://doi.org/10.1016/j.caeo.2021.100053
Ellerton, D. P. (2022). On critical thinking and content knowledge: A critique of the assumptions of cognitive load theory. Thinking Skills and Creativity, 43(Query date: 2024-11-30 12:05:35). https://doi.org/10.1016/j.tsc.2021.100975
Fries, L. (2021). Practicing Connections: A Framework to Guide Instructional Design for Developing Understanding in Complex Domains. Educational Psychology Review, 33(2), 739–762. https://doi.org/10.1007/s10648-020-09561-x
Gill, S. L. (2020). Qualitative Sampling Methods. Journal of Human Lactation, 36(4), 579–581. https://doi.org/10.1177/0890334420949218
Han, J., Xu, K., Yan, Q., Sui, W., Zhang, H., Wang, S., Zhang, Z., Wei, Z., & Han, F. (2022). Qualitative and quantitative evaluation of Flos Puerariae by using chemical fingerprint in combination with chemometrics method. Journal of Pharmaceutical Analysis, 12(3), 489–499. https://doi.org/10.1016/j.jpha.2021.09.003
Hanafi, Y. (2020). Reinforcing public university student’s worship education by developing and implementing mobile-learning management system in the ADDIE instructional design model. International Journal of Interactive Mobile Technologies, 14(2), 215–241. https://doi.org/10.3991/ijim.v14i02.11380
Hanham, J. (2023). Integrating cognitive load theory with other theories, within and beyond educational psychology. British Journal of Educational Psychology, 93(Query date: 2024-11-30 12:05:35), 239–250. https://doi.org/10.1111/bjep.12612
Haryana, M. R. A. (2022). Virtual reality learning media with innovative learning materials to enhance individual learning outcomes based on cognitive load theory. International Journal of Management Education, 20(3). https://doi.org/10.1016/j.ijme.2022.100657
Hendriks, R. A. (2020). Instructional design quality in medical Massive Open Online Courses for integration into campus education. Medical Teacher, 42(2), 156–163. https://doi.org/10.1080/0142159X.2019.1665634
Ji, H., Qin, W., Yuan, Z., & Meng, F. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348, 130698. https://doi.org/10.1016/j.snb.2021.130698
Jiulin, S., Quntao, Z., Xiaojin, G., & Jisheng, X. (2021). Quantitative Evaluation of Top Coal Caving Methods at the Working Face of Extra?Thick Coal Seams Based on the Random Medium Theory. Advances in Civil Engineering, 2021(1), 5528067. https://doi.org/10.1155/2021/5528067
King, R. (2021). How language background impacts learners studying International Financial Reporting Standards: A cognitive load theory perspective. Accounting Education, 30(5), 439–450. https://doi.org/10.1080/09639284.2021.1930562
Klepsch, M. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48(1), 45–77. https://doi.org/10.1007/s11251-020-09502-9
Leppink, J. (2020). Revisiting cognitive load theory: Second thoughts and unaddressed questions. Scientia Medica, 30(1). https://doi.org/10.15448/1980-6108.2020.1.36918
Mahendran, M., Lizotte, D., & Bauer, G. R. (2022). Quantitative methods for descriptive intersectional analysis with binary health outcomes. SSM - Population Health, 17, 101032. https://doi.org/10.1016/j.ssmph.2022.101032
Mamun, M. A. A. (2020). Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers and Education, 144(Query date: 2024-11-30 19:09:04). https://doi.org/10.1016/j.compedu.2019.103695
Mayer, R. E. (2021). Evidence-Based Principles for How to Design Effective Instructional Videos. Journal of Applied Research in Memory and Cognition, 10(2), 229–240. https://doi.org/10.1016/j.jarmac.2021.03.007
Mercader, C. (2020). Factors influencing students’ peer feedback uptake: Instructional design matters. Assessment and Evaluation in Higher Education, 45(8), 1169–1180. https://doi.org/10.1080/02602938.2020.1726283
Mo, C. Y. (2022). Video Playback Speed Influence on Learning Effect From the Perspective of Personalized Adaptive Learning: A Study Based on Cognitive Load Theory. Frontiers in Psychology, 13(Query date: 2024-11-30 12:05:35). https://doi.org/10.3389/fpsyg.2022.839982
Ou, W. J. A. (2022). Writing Accessible Theory in Ecology and Evolution: Insights from Cognitive Load Theory. BioScience, 72(3), 300–313. https://doi.org/10.1093/biosci/biab133
Rim, D. (2021). Effective instructional design template for virtual simulations in nursing education. Nurse Education Today, 96(Query date: 2024-11-30 19:09:04). https://doi.org/10.1016/j.nedt.2020.104624
Rodríguez-Triana, M. J. (2020). Social practices in teacher knowledge creation and innovation adoption: A large-scale study in an online instructional design community for inquiry learning. International Journal of Computer-Supported Collaborative Learning, 15(4), 445–467. https://doi.org/10.1007/s11412-020-09331-5
Roussel, S. (2022). The advantages of listening to academic content in a second language may be outweighed by disadvantages: A cognitive load theory approach. British Journal of Educational Psychology, 92(2). https://doi.org/10.1111/bjep.12468
Ruiz-Rojas, L. I. (2023). Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability (Switzerland), 15(15). https://doi.org/10.3390/su151511524
Sevcenko, N. (2023). Theory-based approach for assessing cognitive load during time-critical resource-managing human–computer interactions: An eye-tracking study. Journal on Multimodal User Interfaces, 17(1), 1–19. https://doi.org/10.1007/s12193-022-00398-y
Shin, S. S. (2020). Structured Query Language Learning: Concept Map-Based Instruction Based on Cognitive Load Theory. IEEE Access, 8(Query date: 2024-11-30 12:05:35), 100095–100110. https://doi.org/10.1109/ACCESS.2020.2997934
Song, C. (2023). Optimizing Foreign Language Learning in Virtual Reality: A Comprehensive Theoretical Framework Based on Constructivism and Cognitive Load Theory (VR-CCL). Applied Sciences (Switzerland), 13(23). https://doi.org/10.3390/app132312557
Sweller, J. (2022). The Role of Evolutionary Psychology in Our Understanding of Human Cognition: Consequences for Cognitive Load Theory and Instructional Procedures. Educational Psychology Review, 34(4), 2229–2241. https://doi.org/10.1007/s10648-021-09647-0
Sweller, J. (2023). The Development of Cognitive Load Theory: Replication Crises and Incorporation of Other Theories Can Lead to Theory Expansion. Educational Psychology Review, 35(4). https://doi.org/10.1007/s10648-023-09817-2
Venkat, M. V. (2020). Using Cognitive Load Theory to Improve Teaching in the Clinical Workplace. MedEdPORTAL?: The Journal of Teaching and Learning Resources, 16(Query date: 2024-11-30 12:05:35), 10983–10983. https://doi.org/10.15766/mep_2374-8265.10983
Vo, M. H. (2020). Students’ performance in blended learning: Disciplinary difference and instructional design factors. Journal of Computers in Education, 7(4), 487–510. https://doi.org/10.1007/s40692-020-00164-7
Wang, X. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107(Query date: 2024-11-30 19:09:04). https://doi.org/10.1016/j.chb.2020.106279
Weng, X. (2023). Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field. Computers and Education: Artificial Intelligence, 4(Query date: 2024-11-30 19:09:04). https://doi.org/10.1016/j.caeai.2022.100117
Zhang, Y. (2020). The Effects of Dynamic Product Presentation and Contextual Backgrounds on Consumer Purchase Intentions: Perspectives from the Load Theory of Attention and Cognitive Control. Journal of Advertising, 49(5), 592–612. https://doi.org/10.1080/00913367.2020.1789014
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Copyright (c) 2024 Rudy Surbakti, Satria Evans Umboh, Ming Pong, Sokha Dara
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