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Scientific Innovation in Asia

Volume 2, 2024 - Issue 1

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A study of the effect of cognitive load on the process of knowledge construction of university students of computer scienc

by Li Wu 1  and  Youfang Li 1
1
Nanchang Film and Television Communication Vocational College, Nanchang City, Jiangxi , China
*
Author to whom correspondence should be addressed.
SIA  2024 2(1):42; https://doi.org/10.12410/sia0201030
Received: 16 October 2024 / Accepted: 27 November 2024 / Published Online: 15 December 2024
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Abstract

Computer Science is a subject with a high degree of logical complexity and extremely abstract and complex specialised concepts. Computer science students need to deal with a large amount of complex information when learning. Cognitive load becomes an important factor that affects students' learning outcomes and hinders the establishment and retention of their knowledge systems. My present study takes clarifying the specific role of cognitive load on the construction of computer science students' knowledge system as a starting point to find ways that can help students regulate cognitive load effectively. My main focus this time was to investigate real and effective practical ways for students to reduce their cognitive load while studying computer science by using qualitative interviews. I conducted in-depth interviews with 10 separate computer science students and used thematic analysis to carefully and deeply analyse the data collected. The study showed that students were able to effectively reduce cognitive load and improve their understanding and long-term memory effects of complex concepts through segmented processing, visual aids, and teamwork. This study points out the importance of managing students‘ cognitive load in computer science education practice, and provides useful references for educators to improve teaching methods and enhance students’ learning outcomes.

Keywords: Cognitive load; Knowledge construction; Computer science education; Learning strategies; Memory retention;
Copyright: © 2024 by Wu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Wu, L.; Li, Y. A study of the effect of cognitive load on the process of knowledge construction of university students of computer scienc. Scientific Innovation in Asia, 2024, 2, 42. doi:10.12410/sia0201030
AMA Style
Wu L, Li Y. A study of the effect of cognitive load on the process of knowledge construction of university students of computer scienc. Scientific Innovation in Asia; 2024, 2(1):42. doi:10.12410/sia0201030
Chicago/Turabian Style
Wu, Li; Li, Youfang 2024. "A study of the effect of cognitive load on the process of knowledge construction of university students of computer scienc" Scientific Innovation in Asia 2, no.1:42. doi:10.12410/sia0201030

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References

  1. CaiHuiying, & GuXiaoqing. (2019). Supporting collaborative learning using a diagram‐based visible thinking tool based on cognitive load theory. British Journal of Educational Technology, 2329-2345.
  2. EllertonPeter. (2022). On critical thinking and content knowledge: A critique of the assumptions of cognitive load theory. Thinking Skills and Creativity, 100975.
  3. GanWenbin, & SunYuan. (2020). Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing. Applied Intelligence, 3894-3912.
  4. GinnsPaul, & LeppinkJimmie. (2019). Special Issue on Cognitive Load Theory: Editorial. Educational Psychology Review, 255-259.
  5. HanYu, & DiaoYongsheng. (2021). Immersive technology-driven investigations on influence factors of cognitive load incurred in construction site hazard recognition, analysis and decision making. Advanced Engineering Informatics, 101298.
  6. KrieglsteinFelix, & BeegeMaik. (2023). Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load. Educational Psychology Review, 9.
  7. Mutlu-BayraktarDuygu, & CosgunVeysel. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 103618.
  8. OgunseijuOmobolanle, & AkanmuAbiola. (2024). Cognitive Load within Mixed Reality Learning Environment in Construction Education. American Society of Civil Engineers, 466-475.
  9. OuyangYewei, & ChengCheng. (2023). The Design of a Posture Instruction Atlas and the Prevention of Construction Workers’ Work-Related Musculoskeletal Disorders (WMSDs): A Study on Attention Allocation and Cognitive Load Based on Eye Tracking. Sustainability , 14207.
  10. ShiYangming, & DuJing. (2020). The Impact of Engineering Information Formats on Workers’ Cognitive Load in Working Memory Development. Construction Research Congress 2020, 79-88.
  11. StillerKlaus Dieter, & SchwormSilke. (2019). Game-based learning of the structure and functioning of body cells in a foreign language: Effects on motivation, cognitive load, and performance. Frontiers in Education, 18.
  12. WangCixiao, & FangTing. (2020). Learning performance and behavioral patterns of online collaborative learning: Impact of cognitive load and affordances of different multimedia. Computers & Education, 103683.
  13. Chandler, P., & Sweller, J. (2021). Reducing cognitive load in the classroom. Educational Psychology Review, 33(3), 1-18.
  14. Kafai, Y. B., Fields, D. A., & Burke, Q. (2021). Collaborative coding environments: Fostering deeper understanding. Journal of Computer Science Education, 35(4), 217–236.
  15. Kalyuga, S. (2020). Managing complexity in learning environments: Cognitive load principles. Instructional Science, 48(2), 131-150.
  16. Lajoie, S., et al. (2022). Adaptive learning technologies for personalized instruction. Computers & Education, 180, 104432.
  17. Mayer, R. E. (2021). Visual and verbal learning in complex domains. Learning and Instruction, 72, 101246.
  18. Sun, Y., et al. (2020). The role of gamification in computer science education. Journal of Educational Technology & Society, 23(2), 76–90.
  19. Sweller, J., Ayres, P., & Kalyuga, S. (2019). Cognitive Load Theory. Springer.
  20. van Merriënboer, J. J. G., & Kirschner, P. A. (2021). Fostering schema acquisition in education. Applied Cognitive Psychology, 35(1), 3–17.