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Articles

Neuroimaging tools in multimedia learning: a systematic review

ORCID Icon, ORCID Icon, , &
Pages 4865-4882 | Received 26 Jun 2021, Accepted 16 Sep 2021, Published online: 10 Oct 2021

References

  • Aaslid, R., Markwalder, T.-M., & Nornes, H. (1982). Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. Journal of Neurosurgery, 57(6), 769–774. https://doi.org/10.3171/jns.1982.57.6.0769
  • Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428. https://doi.org/10.1016/j.compedu.2018.06.023
  • 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. https://doi.org/10.1080/00461520.2018.1554484
  • Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using Electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438. https://doi.org/10.1007/s10648-010-9130-y
  • Artoni, F., Delorme, A., & Makeig, S. (2018). Applying dimension reduction to EEG data by Principal Component analysis reduces the quality of its subsequent Independent Component decomposition. NeuroImage, 175, 176–187. https://doi.org/10.1016/j.neuroimage.2018.03.016
  • Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Systems, 16(6), 345–379. https://doi.org/10.1007/s00530-010-0182-0
  • Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53–61. https://doi.org/10.1207/S15326985EP3801_7
  • Cao, X., Cheng, M., Xue, X., & Zhu, S. (2019). Effects of lecture video types on student learning: An analysis of eye-tracking and electroencephalography data. In F. Xhafa, S. Patnaik, & M. Tavana (Eds.), Advances in intelligent, interactive systems and applications (pp. 498–505). Springer International Publishing. https://doi.org/10.1007/978-3-030-02804-6_66
  • Castro-Alonso, J. C., Ayres, P., & Sweller, J. (2019). Instructional visualizations, cognitive load theory, and visuospatial processing. In J. C. Castro-Alonso (Ed.), Visuospatial processing for education in health and natural sciences (pp. 111–143). Springer. https://doi.org/10.1007/978-3-030-20969-8_5
  • 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. Scopus. https://doi.org/10.1007/s11423-019-09681-4
  • Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324. https://doi.org/10.1016/j.chb.2008.12.020
  • Coelli, S., Sclocco, R., Barbieri, R., Reni, G., Zucca, C., & Bianchi, A. M. (2015, August 25–29). EEG-based index for engagement level monitoring during sustained attention. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy (pp. 1512–1515). IEEE. https://doi.org/10.1109/EMBC.2015.7318658
  • Coverdale, N. S., Gati, J. S., Opalevych, O., Perrotta, A., & Shoemaker, J. K. (2014). Cerebral blood flow velocity underestimates cerebral blood flow during modest hypercapnia and hypocapnia. Journal of Applied Physiology, 117(10), 1090–1096. https://doi.org/10.1152/japplphysiol.00285.2014
  • Dan, A., & Reiner, M. (2017a). EEG-based cognitive load of processing events in 3D virtual worlds is lower than processing events in 2D displays. International Journal of Psychophysiology, 122, 75–84. https://doi.org/10.1016/j.ijpsycho.2016.08.013
  • Dan, A., & Reiner, M. (2017b). Real-time EEG based measurements of cognitive load indicates mental states during learning. JEDM | Journal of Educational Data Mining, 9(2), 31–44. https://doi.org/10.5281/zenodo.3554719.
  • Dan, A., & Reiner, M. (2018). Reduced mental load in learning a motor visual task with virtual 3D method. Journal of Computer Assisted Learning, 34(1), 84–93. https://doi.org/10.1111/jcal.12216
  • DeLeeuw, K. E., & Mayer, R. E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223–234. https://doi.org/10.1037/0022-0663.100.1.223
  • Díaz, D., Ramírez, R., & Hernández-Leo, D. (2015). The effect of using a talking head in academic videos: An EEG study. 2015 IEEE 15th International Conference on Advanced Learning Technologies, 367–369. https://doi.org/10.1109/ICALT.2015.89
  • Dressler, O., Schneider, G., Stockmanns, G., & Kochs, E. F. (2004). Awareness and the EEG power spectrum: Analysis of frequencies. British Journal of Anaesthesia, 93(6), 806–809. https://doi.org/10.1093/bja/aeh270
  • 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. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.00040
  • Glover, G. H. (2011). Overview of functional magnetic resonance imaging. Neurosurgery Clinics, 22(2), 133–139. https://doi.org/10.1016/j.nec.2010.11.001
  • Harner, R. N. (1990). Singular value decomposition—a general linear model for analysis of multivariate structure in the electroencephalogram. Brain Topography, 3(1), 43–47. https://doi.org/10.1007/BF01128860
  • Jirayucharoensak, S., Pan-Ngum, S., & Israsena, P. (2014, September 1). EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. The Scientific World Journal. https://doi.org/10.1155/2014/627892
  • Jo, T., Nho, K., & Saykin, A. J. (2019). Deep learning in Alzheimer’s disease: Diagnostic classification and prognostic prediction using neuroimaging data. Frontiers in Aging Neuroscience, 11. https://doi.org/10.3389/fnagi.2019.00220.
  • Jobsis, F. F. (1977). Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science, 198(4323), 1264–1267. https://doi.org/10.1126/science.929199
  • Kablan, Z., & Erden, M. (2008). Instructional efficiency of integrated and separated text with animated presentations in computer-based science instruction. Computers & Education, 51(2), 660–668. https://doi.org/10.1016/j.compedu.2007.07.002
  • Kakkos, I., Dimitrakopoulos, G. N., Gao, L., Zhang, Y., Qi, P., Matsopoulos, G. K., Thakor, N., Bezerianos, A., & Sun, Y. (2019). Mental workload drives different reorganizations of functional cortical connectivity between 2D and 3D simulated flight experiments. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(9), 1704–1713. https://doi.org/10.1109/TNSRE.2019.2930082
  • Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13(4), 351–371. https://doi.org/10.1002/(SICI)1099-0720(199908)13:4<351::AID-ACP589>3.0.CO;2-6
  • Kannathal, N., Puthusserypady, S. K., & Min, L. C. (2004). Complex dynamics of epileptic EEG. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1, 604–607. https://doi.org/10.1109/IEMBS.2004.1403230
  • Karabatak, M. (2015). A new classifier for breast cancer detection based on Naïve Bayesian. Measurement, 72, 32–36. https://doi.org/10.1016/j.measurement.2015.04.028
  • Khosrowabadi, R., Quek, C., Ang, K. K., & Wahab, A. (2014). ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal. IEEE Transactions on Neural Networks and Learning Systems, 25(3), 609–620. https://doi.org/10.1109/TNNLS.2013.2280271
  • Kıymık, M. K., Güler, İ, Dizibüyük, A., & Akın, M. (2005). Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Computers in Biology and Medicine, 35(7), 603–616. https://doi.org/10.1016/j.compbiomed.2004.05.001
  • Kruger, J.-L., & Doherty, S. (2016). Measuring cognitive load in the presence of educational video: Towards a multimodal methodology. Australasian Journal of Educational Technology, 32(6). https://doi.org/10.14742/ajet.3084.
  • Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208. https://doi.org/10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
  • Lai, J. W. M., & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133, 27–42. https://doi.org/10.1016/j.compedu.2019.01.010
  • Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1
  • Li, Y., & Wu, H. (2012). A clustering method based on K-Means algorithm. Physics Procedia, 25, 1104–1109. https://doi.org/10.1016/j.phpro.2012.03.206
  • Liu, C.-J., & Chiang, W.-W. (2014). Theory, method and practice of neuroscientific findings in science education. International Journal of Science and Mathematics Education, 12(3), 629–646. https://doi.org/10.1007/s10763-013-9482-0
  • Liu, C., Wang, R., Li, L., Ding, G., Yang, J., & Li, P. (2020). Effects of encoding modes on memory of naturalistic events. Journal of Neurolinguistics, 53, 100863. https://doi.org/10.1016/j.jneuroling.2019.100863
  • Loftus, J. J., Jacobsen, M., & Wilson, T. D. (2018). The relationship between spatial ability, cerebral blood flow and learning with dynamic images: A transcranial Doppler ultrasonography study. Medical Teacher, 40(2), 174–180. https://doi.org/10.1080/0142159X.2017.1395401
  • Machado, J., Balbinot, A., & Schuck, A. (2013). A study of the Naive Bayes classifier for analyzing imaginary movement EEG signals using the Periodogram as spectral estimator. 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC) (pp. 1–4). https://doi.org/10.1109/BRC.2013.6487514
  • Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236. Scopus. https://doi.org/10.1016/j.learninstruc.2017.12.007
  • Marshall, S. P. (2007). Identifying cognitive state from eye metrics. Aviation, Space, and Environmental Medicine, 78(Suppl. 5), B165–B175.
  • Mayer, R. (2014a). Cognitive theory of multimedia learning. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–72). Cambridge University Press.
  • Mayer, R. (2014b). Introduction to multimedia learning. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 1–24). Cambridge University Press.
  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
  • Mutlu-Bayraktar, D., Cosgun, V., & Altan, T. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 141, 103618. https://doi.org/10.1016/j.compedu.2019.103618
  • Mutlu-Bayraktar, D., Ozel, P., Altindis, F., & Yilmaz, B. (2020). Relationship between objective and subjective cognitive load measurements in multimedia learning. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1833042
  • Oakley, A. (2012). Foreword. In D. Gough, S. Oliver, & J. Thomas (Eds.), An introduction to systematic reviews (pp. 7–10). SAGE Publications.
  • Örün, Ö, & Akbulut, Y. (2019). Effect of multitasking, physical environment and electroencephalography use on cognitive load and retention. Computers in Human Behavior, 92, 216–229. https://doi.org/10.1016/j.chb.2018.11.027
  • Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
  • Paas, F. G. W. C., van Merriënboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79(1), 419–430. https://doi.org/10.2466/pms.1994.79.1.419
  • Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.
  • Pikovsky, A., Kurths, J., Rosenblum, M., & Kurths, J. (2003). Synchronization: A universal concept in nonlinear sciences. Cambridge University Press.
  • Qayyum, A., Khan, M. K. A. A., Mazher, M., & Suresh, M. (2018). Classification of EEG learning and resting states using 1D-convolutional neural network for cognitive load assesment. 2018 IEEE Student Conference on Research and Development (SCOReD) (pp. 1–5). https://doi.org/10.1109/SCORED.2018.8711150
  • Saby, J. N., & Marshall, P. J. (2012). The utility of EEG band power analysis in the study of infancy and early childhood. Developmental Neuropsychology, 37(3), 253–273. https://doi.org/10.1080/87565641.2011.614663
  • Scharinger, C. (2018). Fixation-related EEG frequency band power analysis: A promising methodology for studying instructional design effects of multimedia learning material. Frontline Learning Research, 6(3), 57–71. https://doi.org/10.14786/flr.v6i3.373
  • Shi, Y., Ruiz, N., Taib, R., Choi, E., & Chen, F. (2007). Galvanic skin response (GSR) as an index of cognitive load. In Proceedings of the SIG CHI Conference on Human Factors in Computing Systems (pp. 2651–2656).
  • Subasi, A., & Ismail Gursoy, M. (2010). EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications, 37(12), 8659–8666. https://doi.org/10.1016/j.eswa.2010.06.065
  • Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
  • Sweller, J., van Merrienboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
  • Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
  • Tsoulos, I. G., Mitsi, G., Stavrakoudis, A., & Papapetropoulos, S. (2019). Application of machine learning in a Parkinson’s disease digital biomarker dataset using Neural Network Construction (NNC) methodology discriminates patient motor status. Frontiers in ICT, 6. https://doi.org/10.3389/fict.2019.00010
  • Uysal, M. P. (2016). Evaluation of learning environments for object-oriented programming: Measuring cognitive load with a novel measurement technique. Interactive Learning Environments, 24(7), 1590–1609. https://doi.org/10.1080/10494820.2015.1041400
  • Wei, Z., Wu, C., Wang, X., Supratak, A., Wang, P., & Guo, Y. (2018). Using support vector machine on EEG for advertisement impact assessment. Frontiers in Neuroscience, 12. https://doi.org/10.3389/fnins.2018.00076.
  • Whelan, R. R. (2007). Neuroimaging of cognitive load in instructional multimedia. Educational Research Review, 2(1), 1–12. https://doi.org/10.1016/j.edurev.2006.11.001
  • Wróbel, A. (2000). Beta activity: A carrier for visual attention. Acta Neurobiologiae Experimentalis, 60(2), 247–260.
  • Zhou, Y., Xu, T., Cai, Y., Wu, X., & Dong, B. (2017). Monitoring cognitive workload in online videos learning through an EEG-based brain-computer interface. In P. Zaphiris, & A. Ioannou (Eds.), Learning and collaboration technologies. Novel learning ecosystems (pp. 64–73). Springer International Publishing. https://doi.org/10.1007/978-3-319-58509-3_7
  • Zolfaghari, F., Khosravi, H., Shahriyari, A., Jabbari, M., & Abolhasani, A. (2019). Hierarchical cluster analysis to identify the homogeneous desertification management units. PLOS ONE, 14(12), e0226355. https://doi.org/10.1371/journal.pone.0226355

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