Improving Business IT learning outcomes using Cognitive Load Optimization – a case study in Chinese Graduate studies

Main Article Content

Stanislaw Paul Maj


This study aims to assess the effectiveness of implementing cognitive load optimization in the instruction of STEM subjects within graduate studies, focusing on the perspective of Chinese students. The primary objective is to scrutinize the influence of this approach on both the student's learning performance and engagement levels. Additionally, this research endeavors to evaluate several key factors, including students' expectations regarding their efforts, the inspiration drawn from lecturers, the conducive learning environment, the anticipation of their performance, their personal innovative thinking, and the perceived relative benefits of this teaching method. By integrating cognitive load optimization strategies into the teaching of STEM disciplines to graduate students, the study seeks to augment their learning performance and enhance their engagement in the subject matter. Particularly, in disciplines like business studies, specialized streams such as IT management may necessitate comprehension of intricate STEM concepts like cybersecurity. Often, business students lack a technical background, resulting in their instruction being reliant on rote memorization of technical information. However, this superficial learning approach often leaves students with an inadequate grasp of the technologies, limiting their applicability in real-world scenarios. To address this challenge, Cognitive Load Optimization (CLO) methodology is employed, converting intricate technical knowledge into easily assimilated mental schemas. These schemas offer the most efficient cognitive pathways for learning, minimizing cognitive load. They serve as the foundation for instructional design and teaching, providing students with structured frameworks for understanding complex concepts. Implementing CLO has demonstrated significant enhancements in learning outcomes, even when dealing with demanding remote and online learning modalities. In this study, a cohort of Chinese graduate students engaged in remote learning were instructed in an IT unit using CLO principles. Their learning experiences were evaluated across six parameters, yielding remarkably high results for five parameters and a high result for the remaining parameter. These findings underscore the potential of cognitive load optimization in enhancing the learning experiences of students, particularly in challenging learning environments.


Download data is not yet available.

Article Details

How to Cite
Nuangjamnong, C., & Maj, S. P. (2023). Improving Business IT learning outcomes using Cognitive Load Optimization – a case study in Chinese Graduate studies. AU-GSB E-JOURNAL, 16(2), 167-179.
Author Biographies

Chompu Nuangjamnong

Professor, Assumption University, Thailand

Stanislaw Paul Maj


Graduate School of Business and Advanced Technology Management, Assumption University of Thailand


Baddeley, A. (2010). Working memory. Current Biology : CB, 20(4), R136-40.

Chong, T. S. (2005). Recent Advances in Cognitive Load Theory Research: Implications for Instructional Designers. Malaysian Online Journal of Instructional Technology (MOJIT), 2(3), 106–117.

de Bruin, A. B. H., & van Merrienboer, J. J. G. (2017). ridging Cognitive Load and Self-Regulated Learning Research: A complementary approach to contemporary issues in educational research. Learning and Instruction, 51, 1–9.

de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science, 38(2), 105–134.

Donovan, S. (2012). FY 2012 Agency Financial Report. 226.

Hossain, M. S., & Nuangjamnong, C. (2021). Development of E-Readiness Scale in Blended Learning in Filmmaking Program for a Private University in Bangladesh – Initial Stage. ABAC ODI JOURNAL Vision. Action. Outcome, 9(1), 162–180.

Hoz, R., Bowman, D., & Kozminsky, E. (2001). The differential effects of prior knowledge on learning: A study of two consecutive courses in earth sciences. Instructional Science, 29(3), 187–211.

Kirschner, P. A., Ayres, P., & Chandler, P. (2011). Contemporary cognitive load theory research: The good, the bad and the ugly. Computers in Human Behavior, 27(1), 99–105.

Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17(2), 248–294.

Leppink, J. (2017). Cognitive load theory: Practical implications and an important challenge. Journal of Taibah University Medical Sciences, 12(5), 385–391.

Maj, S. P. (2018). Cognitive Load Optimization - A New, Practical, Easy-to-Use Teaching Method for Enhancing STEM Educational Outcomes Based on the Science of Learning. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 142–147.

Maj, S. P. (2020). A Statistical Evaluation for three STEM disciplines. The IEEE International Conference on Teaching, Assessment, and Learning for Engineering (IEEE TALE2020); December 8 - 12; Takamatsu, Japan.

Maj, S. P. & Nuangjamnong, C. (2020). Using Cognitive Load Optimization to teach STEM Disciplines to Business Students, 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 428-435, doi: 10.1109/TALE48869.2020.9368351.

Maj, S. P. (2022). A Practical New 21st Century Learning Theory for Significantly Improving STEM Learning Outcomes at all Educational Levels. Eurasia Journal of Mathematics, Science and Technology Education, 18(2), em2073.

Maj, S. P., & Nuangjamnong, C. (2020). Using Cognitive Load Optimiztion to teach STEM Disciplines to Business Students. The IEEE International Conference on Teaching, Assessment, and Learning for Engineering (IEEE TALE2020); December 8 - 12; Takamatsu, Japan.

Marcus, N., Cooper, M., & Sweller, J. (1996). Understanding Instructions. Journal of Educational Psychology, 88(1), 49–63.

McVee, M. B., Dunsmore, K., & Gavelek, J. R. (2005). Schema Theory Revisited. Review of Educational Research, 75(4), 531–566.

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.

Moidunny, K. (2009). The Effectiveness of the National Professional Qualification for Educational Leaders (NPQEL). Unpublished Doctoral Dissertation, Bangi: The National University of Malaysia.

Nakhleh, M. B. (1992). Why some students don't learn chemistry: Chemical misconceptions. Journal of chemical education, 69(3), 191.

NSF makes new awards to advance Science of Learning | NSF - National Science Foundation. (n.d.). Retrieved January 3, 2021, from

Nuangjamnong, C., & Maj, S. P. (2022). Students’ Behavioral Intention to Adopt Cognitive Load Optimization to Teach STEM in Graduate Studies. Journal of Education Naresuan University, 24(3), 24–43.

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38(1), 1–4.

Paas, F., & van Gog, T. (2006). Optimising worked example instruction: Different ways to increase germane cognitive load. In Learning and Instruction (Vol. 16, Issue 2 SPEC. ISS., pp. 87–91). Elsevier BV.

Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61–86.

Reedy, G. B. (2015). Using Cognitive Load Theory to Inform Simulation Design and Practice. Clinical Simulation in Nursing, 11(8), 355–360.

Rodprayoon, N., Nuangjamnong, C., & MAJ, S. P. (2017). Distance Learning – A Potential Opportunity for Thailand. Modern Applied Science, 11(11), 20.

Rogers, E.M. (1995), Diffusion of Innovation, 4th ed., Free Press, New York, NY.

Science of Learning | NSF - National Science Foundation. (n.d.). Retrieved January 3, 2021, from

Science of Learning Research Centre Home - Science of Learning Research Centre. (n.d.). Retrieved January 3, 2021, from

Sweller, J. (2010). Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load. Educational Psychology Review, 22(2), 123–138.

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Categories of Knowledge: An Evolutionary Approach. In: Cognitive Load Theory. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies. Springer.

Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251–296.

Tan, E., Calabrese Barton, A., Kang, H., & O’Neill, T. (2013). Desiring a career in STEM-related fields: How middle school girls articulate and negotiate between their narrated and embod- ied identities in considering a STEM trajectory. Journal of Research in Science Teaching, 50(10), 1143–1179.

Tertiary Education Quality and Standards Agency (TEQSA). (2022). New TEQSA report details student experiences of switch to online learning. (30 November 2020).

Valcke, M. (2002). Cognitive load: updating the theory? Learning and Instruction, 12, 147–154.

van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. Educational Psychology Review, 17(2), 147–177.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.

Wahyudi, S., & Aqidawati, E. F. (2019). Learning a Supply Chain Management Course by Problem Based Learning: Case Studies in the Newspaper Industry. Proceedings of the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, 2019, 3559–3570.

Wang, L., Hossain, M. S., & Nuangjamnong, C. (2022). The Differences of Students Traits in Computer Science Program with the Perception of Using Laptops for Studying in Chengdu, Sichuan, China. AU-GSB e-Journal, 15(1), 164–173.

Weinstein, Y., Madan, C. R., & Sumeracki, M. A. (2018). Teaching the science of learning. Cognitive Research: Principlesand Implications, 3(2), 1–17.

Wouters, P., Paas, F., & van Merriënboer, J. J. G. (2008). How to Optimize Learning from Animated Models: A Review of Guidelines Based on Cognitive Load. Review of Educational Research, 78(3), 645–675.

Most read articles by the same author(s)

1 2 > >>