Cognitive Load Theory on Virtual Mathematics Laboratory: Systematic Literature Review
The primary goal of cognitive load theory is to improve the learning of complex cognitive tasks by transforming current scientific knowledge on how cognitive structures and processes are organized into guidelines for instructional design. Cognitive load theory assumes that the bottleneck for acquiring new secondary biological knowledge is the limited working memory capacity. In the ideal situation, the working memory resources required for learning do not exceed the available resources. Despite this, in reality, there will often be a high cognitive load, or even ”overload,” for two reasons. First, dealing with interactive information elements in complex cognition imposes a high intrinsic working memory load. Second, learners also have to use working memory resources for activities that are extraneous to performing and learning tasks, that is, activities that are not productive for learning. Virtual Laboratory is a form of animation that can visualize abstract phenomena or complex experiments in natural laboratories to increase learning activities and develop problem-solving skills. A virtual math laboratory was created to optimize dual coding memory, namely verbal and audio learning. The investigation tracked the approved reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) guidelines, illustrating the outcomes of the literature searches and articles selection process. It is used to provide that the selection process is replicable and transparent. We accomplished a computerized bibliometric analysis from 2002-2022 for articles retrieved from the SCOPUS database. Data were collected in July 2022.
Keywords: cognitive load theory, virtual laboratory, mathematics education
 Schnotz W, Kürschner C. A reconsideration of cognitive load theory. Educational Psychology Review. 2007;19:469–508.
 Kirschner PA, Sweller J, Kirschner F, Zambrano RJ. From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning. 2018;13:213–233.
 Paas F, Renkl A, Sweller J. Cognitive load theory and instructional design: Recent developments. Educational Psychologist. 2003;38:1–4.
 Longo L. Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning. 2022. arXiv preprint arXiv:2209.10992.
 Sweller J. Cognitive load theory and educational technology. Educational Technology Research and Development. 2020;68:1–6.
 Sweller J. Instructional design in technical areas. Camberwell. Victoria: ACER Press; 1999.
 Sweller J. Handbook of research on educational communications and technology. England, UK: Routledge; 2008. Human cognitive architecture. p. 369–381.
 Sweller J, Van Merrienboer JJ, Paas FG. Cognitive architecture and instructional design. Educational Psychology Review. 1998;10:251–296.
 Kalyuga S. Cognitive load theory: How many types of load does it really need? Educational Psychology Review. 2011;23:1–9.
 Kirschner PA, Sweller J, Kirschner F, Zambrano JR. From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning. 2018;13:213–233.
 Kirschner PA, Sweller J, Kirschner F, Zambrano R J. From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning. 2018;13:213–233.
 Kirschner PA. Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction. 2002;12:1–10.
 Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of Clinical Epidemiology. 2009;62:e1–34.
 Aria M, Cuccuracolo C. Bibliometrics: An R-tool for comprehensive science mapping analysis. Journal of Informetrics. 2017;11:959–975.
 Chandler P, Sweller J. Cognitive load theory and the format of instruction. Cognition and Instruction. 1991;8:293–332.
 Chen O, Retnowati E, Kalyuga S. Effects of worked examples on step performance in solving complex problems. Educational Psychology. 2019;39:188–202.
 De Jong T. Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science. 2010;38:105–134.
 Ginns P, Leppink J. Special issue on cognitive load theory. Educational Psychology Review. 2019;31:255–259.
 Haynes CC, Ericson BJ. Problem-solving efficiency and cognitive load for adaptive parsons problems vs. writing the equivalent code. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021:1–15.
 Hoffman B. “I think I can, but I’m afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences. 2010;20:276–283.
 Hoffman B, Spatariu A. The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology. 2008;33:875–893.
 Muhtarom, Zuhri MS, Herlambang BA, Murtianto YH. Comparison of students’ mathematics critical thinking skills through the use of learning management systems and whatsapp groups in online learning. AIP Conference Proceedings. 2022;2577:020036.