A Practical New 21st Century Learning Theory for Significantly Improving STEM Learning Outcomes at all Educational Levels

Stanislaw Paul Maj

doi:10.29333/ejmste/11510

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Stanislaw Paul Maj ¹ ² ^*

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¹ SPM Consulting, AUSTRALIA
² Assumption University, THAILAND
^* Corresponding Author

Abstract

Current educational theories, such as Constructivism, are based on subjective principles which are open to interpretation and may result in wide variations in the quality of learning outcomes. Cognitive Load Optimization is a new quantitative, Science of Learning theory. Using this method, it is possible to define schemas (mental patterns of knowledge) with the quantitatively optimized minimum cognitive load which are the basis of instructional material development. Such materials represent the easiest possible learning paths. In one proof of concept experiment this new method was used to teach first year undergraduate mathematics to mature students in online mode. The pass rate and retention rates were both 100% and all learning objectives were met. Extensive evaluations strongly suggest that Cognitive Load Optimization can significantly improve teaching and learning outcomes in all STEM disciplines at all educational levels – primary and secondary school, college and university.

Keywords

cognitive load optimization
cognitive science
learning theories
science of learning

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.29333/ejmste/11510

EURASIA J Math Sci Tech Ed, 2022 - Volume 18 Issue 2, Article No: em2073

Publication date: 10 Jan 2022

Article Views: 1143

Article Downloads: 522

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Disclosure

Declaration of Conflict of Interest: No conflict of interest is declared by author(s).

Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author(s).

References

Bannert, M. (2002). Managing cognitive load - recent trends in cognitive load theory. Learning and Instruction, 12(1), 139-146. https://doi.org/10.1016/S0959-4752(01)00021-4
Berliner, D. C. (2002). Educational research: The hardest science of all. Educational Researcher, 31(8), 18-20. https://doi.org/10.3102/0013189X031008018
Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy (Structure of the observed learning outcome). Academic Press.
Cheng, E. W. L. (2016). Learning through variation theory: A case study. International Journal of Teaching and Learning in Higher Education, 28(2), 283-292.
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science, 38, 105-134. https://doi.org/10.1007/s11251-009-9110-0
Diekhoff, G. M. (1983). Relationship judgements in the evaluation of structural understanding. Journal of Educational Psychology, 75, 227-233. https://doi.org/10.1037/0022-0663.75.2.227
Education. (2006). The research agenda for the new discipline of engineering education. Journal of Engineering Education, 95(4), 259-261. https://doi.org/10.1002/j.2168-9830.2006.tb00900.x
Grover, D., & Winton, S. (2017). Digital technologies for the Australian curriculum. A project-based approach for years 7 and 8. Cengage.
Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21(6), 803-831. https://doi.org/10.1017/S0140525X98001769
Happs, J. C. (1985). Cognitive learning theory and classroom complexity. Research in Science and Technology Education, 3, 159-174. https://doi.org/10.1080/0263514850030109a
Hattie, J. (2004). Cognitive processes in AsTTle: The SOLO taxonomy. Ministry of Education.
Holkner, B., Romeo, G., Henderson, M., Auld, G., Russell, G., Seah, W. T., & Fernando, A. (2008). Exemplar schools using innovative learning technologies. https://research.monash.edu/en/publications/exemplar-schools-using-innovative-learning-technologies
IET. (2008). Studying STEM: What are the barriers. https://studylib.net/doc/10501523/studying-stem--what-are-the-barriers%3F-factfilesI
Impact. (2015). The science of learning. https://deansforimpact.org/resources/the-science-of-learning/
Keppens, J., & Hay, D. (2008). Concept map assessment for teaching computer science programming. Computer Science Education, 18(1), 31-42. https://doi.org/10.1080/08993400701864880
Klausmeirer, H. J. (1992). Concept learning and concept teaching. Educational Psychologist, 27(3), 267-289. https://doi.org/10.1207/s15326985ep2703_1
Kolling, M. (2010). The greenfoot programming environment. ACM Transactions on Computing Education, 10(4), 1-21. https://doi.org/10.1145/1868358.1868361
Kolling, M. (2016). Introduction to programming with greenfoot. Pearson.
Lyons, T. (2004). Choosing physical science courses: The importance of cultural and social capital in enrolment decisions of high achieving students [Paper presentation]. IOSTE X1 Symposium, Lublin, Poland.
Maj, S. P. (2018). Cognitive load optimization - A new, practical, easy-to-use method for enhancing STEM educational outcomes based on the science of learning [Paper presentation]. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Wollongong, Australia. https://doi.org/10.1109/TALE.2018.8615324
Maj, S. P. (2020). Cognitive load optimization - A statistical evaluation for three STEM disciplines [Paper presentation]. IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Takamatsu, Japan. https://doi.org/10.1109/TALE48869.2020.9368430
Maj, S. P. (2021a). Benchmarking educational quality - An independent analysis and alternative approach [Paper presentation]. 38th International Conference on Innovation, Practice & Research in the use of Educational Technologies in Tertiary Education, On line. https://doi.org/10.14742/ascilite2021.0144
Maj, S. P. (2021b). A new 21st century quantitative learning theory for improving STEM education in both face to face and online modes [Paper presentation]. STEM 2021, Vancouver, Canada.
Maj, S. P. (2021c). World class STEM – Benchmarking and delivering based on evidence based cognitive science [Paper presentation]. IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Wuhan, China.
Maj, S. P., & Nuangjamnong, C. (2020). Using cognitive load optimization to teach STEM disciplines to business students [Paper presentation]. IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Takamatsu, Japan. https://doi.org/10.1109/TALE48869.2020.9368351
Maj, S. P., & Veal, D. (2007). State model diagrams as a pedagogical tool - An international evaluation. IEEE Transactions on Education, 50(3), 204-207. https://doi.org/10.1109/TE.2007.900028
Maj, S. P., Kohli, G., & Fetherston, T. (2005). A pedagogical evaluation of new state model diagrams for teaching internetwork technologies [Paper presentation]. 28th Australasian Computer Science Conference, Newcastle, Australia.
Merrill, M. D., & Tennyson, R. D. (1977). Teaching concepts: An instructional design guide. Educational Technology Publications.
Meyer, B. J. F. (1985). Signalling the structure of text. In D. H. Jonassen (Ed.), Technology of text (Vol. 2). Educational Technology Publications.
Nagy, P. (1984). Cognitive structure and the spatial metaphor. In N. P. (Ed.), The representation of cognitive structure. Ontario Institute for Studies in Education.
NSF. (2017). Science of learning. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5567
Nuangjamnong, C., & Maj, S. P. (2022). Students behaviour intention to adopt cognitive load optimization to teach STEM in graduate studies. Journal of Education Naresan University, 24(3).
PCAST. (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pdf
Piaget, J. I. B. (1969). The psychology of the child. Basic Books.
Reigeluth, C. M. (1979). In search of a better way of organising instruction: The elaboration theory. Journal of Instructional Development, 2, 8-15. https://doi.org/10.1007/BF02984374
Skinner, B. F. (1950). Are theories of learning necessary. Psychological Review, 57, 193-216. https://doi.org/10.1037/h0054367
SLRC. (n. d.). Science of learning research centre. https://www.slrc.org.au/
Thickett, G., & Stamell, J. (2012). Science study guide year 8. Get the results you want. Pascal Press.
Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In Cambridge handbook of computational psychology (pp. 23-58). Cambridge University Press. https://doi.org/10.1017/CBO9780511816772.005
Thomson, W. (1889). Electrical units of measurements. In Popular lectures and addresses (pp. 73-136). Cambridge University Press. https://doi.org/10.1017/CBO9780511997242.006
Valcke, M. (2002). Cognitive load: Updating the theory. Learning and Instruction, 12(1), 147-154. https://doi.org/10.1016/S0959-4752(01)00022-6
Weinstein, Y., Madan, C. R., & Sumeracki, M.A. (2018). Teaching the science of learning. Cognitive Research: Principles and Implications, 3, 2. https://doi.org/10.1186/s41235-017-0087-y
Williamson, K., & Garton, A. (2011). Science essentials 8. MacMillan.
Williamson, K., & Garton, A. (2020). Science essentials 8 (Australian curriculum ed.). Colin McNeil.

How to cite this article

APA

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. https://doi.org/10.29333/ejmste/11510

Vancouver

Maj SP. A Practical New 21st Century Learning Theory for Significantly Improving STEM Learning Outcomes at all Educational Levels. EURASIA J Math Sci Tech Ed. 2022;18(2):em2073. https://doi.org/10.29333/ejmste/11510

AMA

Maj SP. A Practical New 21st Century Learning Theory for Significantly Improving STEM Learning Outcomes at all Educational Levels. EURASIA J Math Sci Tech Ed. 2022;18(2), em2073. https://doi.org/10.29333/ejmste/11510

Chicago

Maj, Stanislaw Paul. "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 2022 18 no. 2 (2022): em2073. https://doi.org/10.29333/ejmste/11510

Harvard

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. https://doi.org/10.29333/ejmste/11510

MLA

Maj, Stanislaw Paul "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, vol. 18, no. 2, 2022, em2073. https://doi.org/10.29333/ejmste/11510