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Evaluating the Effects of Educational Multimedia Design Principles on Cognitive Load Using EEG Signal Analysis

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Abstract

Educational multimedia has proven to be an effective and efficient way of learning. Designers strive to produce multimedia that convey concepts most efficiently. That is to design multimedia that imposes the least possible cognitive load on the learner. Mayer’s multimedia design principles are well-known, and multiple pieces of research have proven them to be effective. Reviewing the literature makes it obvious that there is a lack of a neurologically supported measure to express the effectiveness of these principles in the enhancement of the learning process. Mayer has reported the importance of these principles through effect sizes of scores obtained from transfer tests taken from the subjects. In this research, we utilized five of the twelve design principles introduced by Mayer to create With-Principles multimedia. These five principles were signaling, coherence, spatial contiguity, temporal contiguity and redundancy. We selected one chapter from Oxford’s open forum 3 and designed two versions of multimedia (With-Principles and Without-Principles) for the chapter. In one version, we designed the multimedia according to the design principles, and in the other version, no specific design principles were applied. A total number of 28 non-native English speaker students were divided into two groups. One group watched the With-Principles version of the multimedia, and the other group watched the Without-Principles version. NASA-TLX and a final comprehension test accompanied the procedure. Meanwhile, the subjects’ brain signals were being recorded. The results from both the post-task tests and the EEG analysis show that the With-Principles multimedia has imposed a significantly lower cognitive load on the learners. Furthermore, we propose the effectiveness of each principle by measuring the amount to which each principle has contributed to reducing the cognitive load of the subjects during the multimedia. Subjects’ brain signals analysis reveals that the signaling and the spatial contiguity principles have the most effect on learning enhancement.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. Search for Extra-Terrestrial Life

  2. Independent Component Analysis

  3. Artifact Subspace Reconstruction

  4. Power Spectral Density

  5. Fast Fourier Transform

References

  • Anmarkrud, Ø., Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61–83.

    Article  Google Scholar 

  • Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A. (2015). The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in neuroinformatics, 9, 16.

    Article  Google Scholar 

  • Castro-Meneses, L. J., Kruger, J.-L., & Doherty, S. (2020). Validating theta power as an objective measure of cognitive load in educational video. Educational Technology Research and Development, 68(1), 181–202.

    Article  Google Scholar 

  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.

    Book  MATH  Google Scholar 

  • Cohen, M. X. (2014). Analyzing neural time series data: theory and practice. MIT Press.

    Book  Google Scholar 

  • Debie, E., Rojas, R. F., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., & Abbass, H. A. (2021). Multimodal fusion for objective assessment of cognitive workload: A review. IEEE Transactions on Cybernetics, 51(3), 1542–1555. https://doi.org/10.1109/TCYB.2019.2939399

    Article  Google Scholar 

  • Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9–21.

    Article  Google Scholar 

  • Fernandez Rojas, R., Debie, E., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., & Abbass, H. (2020). Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments [Original Research]. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.00040

    Article  Google Scholar 

  • Fink, A., & Benedek, M. (2014). EEG alpha power and creative ideation. Neuroscience & Biobehavioral Reviews, 44, 111–123.

    Article  Google Scholar 

  • Gerbaudo, R., Gaspar, R., & Gonçalves Lins, R. (2021). Novel online video model for learning information technology based on micro learning and multimedia micro content. Education and Information Technologies, 26(5), 5637–5665. https://doi.org/10.1007/s10639-021-10537-9

    Article  Google Scholar 

  • Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology (Vol. 52, pp. 139-183). Elsevier.

  • Herweg, N. A., Solomon, E. A., & Kahana, M. J. (2020). Theta oscillations in human memory. Trends in cognitive sciences, 24(3), 208–227.

    Article  Google Scholar 

  • Homan, R. W., Herman, J., & Purdy, P. (1987). Cerebral location of international 10–20 system electrode placement. Electroencephalography and clinical neurophysiology, 66(4), 376–382.

    Article  Google Scholar 

  • Jahng, J., Kralik, J. D., Hwang, D.-U., & Jeong, J. (2017). Neural dynamics of two players when using nonverbal cues to gauge intentions to cooperate during the Prisoner’s Dilemma Game. NeuroImage, 157, 263–274.

    Article  Google Scholar 

  • Jensen, O., Gelfand, J., Kounios, J., & Lisman, J. E. (2002). Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cerebral cortex, 12(8), 877–882.

    Article  Google Scholar 

  • Karimi-Rouzbahani, H., Bagheri, N., & Ebrahimpour, R. (2017). Average activity, but not variability, is the dominant factor in the representation of object categories in the brain. Neuroscience, 346, 14–28.

    Article  Google Scholar 

  • Karimi-Rouzbahani, H., Bagheri, N., & Ebrahimpour, R. (2017). Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition. Neuroscience, 349, 48–63.

    Article  Google Scholar 

  • Khader, P. H., Jost, K., Ranganath, C., & Rösler, F. (2010). Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neuroscience Letters, 468(3), 339–343.

    Article  Google Scholar 

  • Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12), 606–617.

    Article  Google Scholar 

  • Korbach, A., Brünken, R., & Park, B. (2018). Differentiating different types of cognitive load: A comparison of different measures. Educational Psychology Review, 30(2), 503–529.

    Article  Google Scholar 

  • Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Role of subjective and objective measures of cognitive processing during learning in explaining the spatial contiguity effect. Learning and Instruction, 61, 23–34. https://doi.org/10.1016/j.learninstruc.2018.12.001

    Article  Google Scholar 

  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811678

    Book  Google Scholar 

  • Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423. https://doi.org/10.1111/jcal.12197

    Article  Google Scholar 

  • Miller, J., Watrous, A. J., Tsitsiklis, M., Lee, S. A., Sheth, S. A., Schevon, C. A., Smith, E. H., Sperling, M. R., Sharan, A., & Asadi-Pooya, A. A. (2018). Lateralized hippocampal oscillations underlie distinct aspects of human spatial memory and navigation. Nature Communications, 9(1), 1–12.

    Article  Google Scholar 

  • Naismith, L. M., Cheung, J. J., Ringsted, C., & Cavalcanti, R. B. (2015). Limitations of subjective cognitive load measures in simulation-based procedural training. Medical Education, 49(8), 805–814.

    Article  Google Scholar 

  • Narimani, M., & Soleymani, E. (2013). The effectivenes of cognitive rehabilitation on executive functions (working memory and attention) and academic achievement in students with math learning diworder. Journal of Learning Disabilities, 2(3), 91–115.

    Google Scholar 

  • Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: a cognitive-load approach. Journal of Educational Psychology, 84(4), 429.

    Article  Google Scholar 

  • Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. The Cambridge Handbook of Multimedia Learning, 27, 27–42.

    Article  Google Scholar 

  • Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.

    Article  Google Scholar 

  • Paivio, A. (1990). Mental representations: A dual coding approach. Oxford University Press.

    Book  Google Scholar 

  • Pi, Z., Zhang, Y., Zhou, W., Xu, K., Chen, Y., Yang, J., & Zhao, Q. (2021). Learning by explaining to oneself and a peer enhances learners’ theta and alpha oscillations while watching video lectures. British Journal of Educational Technology, 52(2), 659–679.

    Article  Google Scholar 

  • Sanchiz, M., Lemarié, J., Chevalier, A., Cegarra, J., Paubel, P. V., Salmerón, L., & Amadieu, F. (2019). Investigating multimedia effects on concept map building: Impact on map quality, information processing and learning outcome. Education and Information Technologies, 24(6), 3645–3667. https://doi.org/10.1007/s10639-019-09943-x

    Article  Google Scholar 

  • Solomon, E. A., Lega, B. C., Sperling, M. R., & Kahana, M. J. (2019). Hippocampal theta codes for distances in semantic and temporal spaces. Proceedings of the National Academy of Sciences, 116(48), 24343–24352.

    Article  Google Scholar 

  • Sweller, J. (2011). CHAPTER TWO - cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

    Chapter  Google Scholar 

  • Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292.

    Article  Google Scholar 

  • Tenório, K., Pereira, E., Remigio, S., Costa, D., Oliveira, W., Dermeval, D., da Silva, A. P., Bittencourt, I. I., & Marques, L. B. (2022). Brain-imaging techniques in educational technologies: A systematic literature review. Education and Information Technologies, 27(1), 1183–1212. https://doi.org/10.1007/s10639-021-10608-x

    Article  Google Scholar 

  • Wang, S., Gwizdka, J., & Chaovalitwongse, W. A. (2016). Using Wireless EEG Signals to Assess Memory Workload in the $n$-Back Task. IEEE Transactions on Human-Machine Systems, 46(3), 424–435. https://doi.org/10.1109/THMS.2015.2476818

  • Wang, J., Antonenko, P., Keil, A., & Dawson, K. (2020). Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video. Mind, Brain, and Education, 14(3), 279–291.

    Article  Google Scholar 

  • Whitmarsh, S., Oostenveld, R., Almeida, R., & Lundqvist, D. (2017). Metacognition of attention during tactile discrimination. NeuroImage, 147, 121–129.

    Article  Google Scholar 

Download references

Acknowledgement

This work has been supported by Shahid Rajaee Teacher Training University.

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Authors and Affiliations

  1. Artificial Intelligence Group, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Araz Farkish, Alireza Bosaghzadeh, S. Hamid Amiri & Reza Ebrahimpour

  2. School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran

    Reza Ebrahimpour

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  1. Araz Farkish
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  2. Alireza Bosaghzadeh
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  3. S. Hamid Amiri
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  4. Reza Ebrahimpour
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Correspondence to Alireza Bosaghzadeh.

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Ethical and Consent Statements

All subjects signed an informed written consent before attending the study. The experimental protocols were approved by the ethics committee of the Iran University of Medical Sciences. The Certificate of Approval Number is IR.IUMS.REC.1397.951.

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Farkish, A., Bosaghzadeh, A., Amiri, S.H. et al. Evaluating the Effects of Educational Multimedia Design Principles on Cognitive Load Using EEG Signal Analysis. Educ Inf Technol 28, 2827–2843 (2023). https://doi.org/10.1007/s10639-022-11283-2

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  • DOI: https://doi.org/10.1007/s10639-022-11283-2

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