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15-03-2024 | Original Paper

Video Education through the Lens of Educational Neuroscience: A Concise Review

Authors: Zhicong Zhang, Yuting Gao, Yafeng Pan, Jiaxian Zhou

Published in: TechTrends | Issue 3/2024

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Abstract

To further enhance the understanding of video education, researchers have started leveraging neuroscientific approaches to investigate the underlying cognitive processes and their correlation with learning outcomes. This concise review begins by examining recent behavioral studies that focus on teachers, students, and video media within the video education context. The findings from these studies provide valuable insights into the overall paradigm and design. Moreover, this review explores the potential of single-brain research and the emerging multi-brain scanning approach to shed light on the neural mechanisms involved in video learning. Current research has mostly used electroencephalography (EEG) to detect different brainwave frequency changes when single learners watch videos, as well as functional magnetic resonance or EEG to discover the extent to which the coupling of brain activity between instructional subjects in video-based instruction predicts learning outcomes. These novel approaches hold great promise in determining the cognitive processes implicated in video education and may eventually lead to more personalized and effective learning methods. By synthesizing the current state of research, this review aims to contribute to the ongoing discussions in the field and inspire further investigations into the neuroscientific aspects of video education.

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Aggarwal, S., Lamba, M., Verma, K., Khuttan, S., & Gautam, H. (2021). A preliminary investigation for assessing attention levels for Massive Online Open Courses learning environment using EEG signals: An experimental study. Human Behavior and Emerging Technologies, 3(5), 933–941. https://doi.org/10.1002/hbe2.274

Alimardani, M., van den Braak, S., Jouen, A., Matsunaka, R., & Hiraki, K. (2021). Assessment of Engagement and Learning During Child-Robot Interaction Using EEG Signals. In H. Li, S. S. Ge, Y. Wu, A. Wykowska, H. He, X. Liu, D. Li, & J. Perez-Osorio (Eds.), Social Robotics. ICSR 2021. Lecture Notes in Computer Science (pp. 671–682). Springer International Publishing. https://doi.org/10.1007/978-3-030-90525-5_59

Balters, S., Baker, J. M., Hawthorne, G., & Reiss, A. L. (2020). Capturing Human Interaction in the Virtual Age: A Perspective on the Future of fNIRS Hyperscanning. Frontiers in Human Neuroscience, 14, 558494. https://doi.org/10.3389/fnhum.2020.588494

Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student–teacher dynamics: Evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401–411. https://doi.org/10.1162/jocn_a_01274

Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, 411–417. https://doi.org/10.1016/j.chb.2017.12.002

Boucheix, J., Gauthier, P., Fontaine, J., & Jaffeux, S. (2018). Mixed camera viewpoints improve learning medical hand procedure from video in nurse training? Computers in Human Behavior, 89, 418–429. https://doi.org/10.1016/j.chb.2018.01.017

Brucker, B., de Koning, B., Rosenbaum, D., Ehlis, A., & Gerjets, P. (2022). The influence of gestures and visuospatial ability during learning about movements with dynamic visualizations – An fNIRS study. Computers in Human Behavior, 129, 107151. https://doi.org/10.1016/j.chb.2021.107151

Castro-Meneses, L. J., Kruger, J., & 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. https://doi.org/10.1007/s11423-019-09681-4CrossRef

Cattaneo, A. A. P., van der Meij, H., Aprea, C., Sauli, F., & Zahn, C. (2019). A model for designing hypervideo-based instructional scenarios. Interactive Learning Environments, 27(4), 508–529. https://doi.org/10.1080/10494820.2018.1486860CrossRef

Chen, C., & Wang, J. (2018). Effects of online synchronous instruction with an attention monitoring and alarm mechanism on sustained attention and learning performance. Interactive Learning Environments, 26(4), 427–443. https://doi.org/10.1080/10494820.2017.1341938CrossRef

Chen, C., & Wu, C. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers and Education, 80, 108–121. https://doi.org/10.1016/j.compedu.2014.08.015CrossRef

Chen, C., Wang, J., & Yu, C. (2017). Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. British Journal of Educational Technology, 48(2), 348–369. https://doi.org/10.1111/bjet.12359CrossRef

Cheng, X., Liu, M., Pan, Y., & Li, H. (2021). The teaching and learning brains: Interpersonal neuroscience in educational research. Advances in Psychological Science, 29(10), 1–9. https://doi.org/10.3724/SP.J.1042.2021.001CrossRef

Cheng, P., Chien, Y., & Huang, Y. (2017). The Design and Implementation of a Real-Time Attention Recognition/Feedback System in Online Learning Course. Paper presented at the 2017 International Conference of Educational Innovation through Technology (EITT) (pp:214–217). IEEE. https://doi.org/10.1109/EITT.2017.59.

Christopher, L., & Jamie, C. (2019). The negative impact of media diversity on self-regulated learning strategies and cognitive load. Issues in Educational Research, 29(1), 158–179. https://doi.org/10.3316/ielapa.171932130615998CrossRef

Cohen, S. S., Madsen, J., Touchan, G., Robles, D., Lima, S. F. A., Henin, S., & Parra, L. C. (2018). Neural engagement with online educational videos predicts learning performance for individual students. Neurobiology of Learning and Memory, 155, 60–64. https://doi.org/10.1016/j.nlm.2018.06.011CrossRef

Costley, J., Fanguy, M., Lange, C., & Baldwin, M. (2021). The effects of video lecture viewing strategies on cognitive load. Journal of Computing in Higher Education, 33(1), 19–38. https://doi.org/10.1007/s12528-020-09254-yCrossRef

Dadebayev, D., Goh, W. W., & Tan, E. X. (2021). EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques. Journal of King Saud University - Computer and Information Sciences (In Press). https://doi.org/10.1016/j.jksuci.2021.03.009CrossRef

Davidesco, I. (2020). Brain-to-Brain Synchrony in the STEM Classroom. CBE Life Sciences Education, 19(3), es8. https://doi.org/10.1187/cbe.19-11-0258CrossRef

Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers and Education, 78, 312–320. https://doi.org/10.1016/j.compedu.2014.06.018CrossRef

Desai, T. S., & Kulkarni, D. C. (2022). Assessment of interactive video to enhance learning experience: a case study. Journal of Engineering Education Transformations, 35(Special Issue 1), 74–80.CrossRef

Dinmore, S. (2019). Beyond lecture capture: Creating digital video content for online learning–a case study. Journal of University Teaching and Learning Practice, 16(1), 7. https://doi.org/10.53761/1.16.1.7CrossRef

Dizon, G., & Gayed, J. M. (2021). An exploratory study on the use of interactive video via Netflix to improve second language aural vocabulary learning Gilbert Dizon1, John M. Gayed2. AsiaCALL Online Journal, 12(5), 100–113. https://doi.org/10.11251/acoj.12.05.007CrossRef

Fanguy, M., Costley, J., Baldwin, M., Lange, C., & Wang, H. (2019). Diversity in video lectures: Aid or hindrance? International Review of Research in Open and Distributed Learning, 20(2), 43–62. https://doi.org/10.19173/irrodl.v20i2.3838CrossRef

Farkish, A., Bosaghzadeh, A., Amiri, S. H., & Ebrahimpour, R. (2023). Evaluating the effects of educational multimedia design principles on cognitive load using eeg signal analysis. Education and Information Technologies, 28(3), 2827–2843. https://doi.org/10.1007/s10639-022-11283-2CrossRef

Fiorella, L., van Gog, T., Hoogerheide, V., & Mayer, R. E. (2017). It’s all a matter of perspective: Viewing first-person video modeling examples promotes learning of an assembly task. Journal of Educational Psychology, 109(5), 653–665. https://doi.org/10.1037/edu0000161CrossRef

Fiorella, L., Stull, A. T., Kuhlmann, S., & Mayer, R. E. (2019). Instructor presence in video lectures: The role of dynamic drawings, eye contact, and instructor visibility. Journal of Educational Psychology, 111(7), 1162–1171. https://doi.org/10.1037/edu0000325CrossRef

Fiorella, L., Stull, A. T., Kuhlmann, S., & Mayer, R. E. (2020). Fostering generative learning from video lessons: Benefits of instructor-generated drawings and learner-generated explanations. Journal of Educational Psychology, 112(5), 895–906. https://doi.org/10.1037/edu0000408CrossRef

Gao, Q., & Tan, Y. (2022). Impact of different styles of online course videos on students’ attention during the Covid-19 pandemic. Frontiers in Public Health, 10, 858780. https://doi.org/10.3389/fpubh.2022.858780CrossRef

Garrett, N. (2021). Segmentation’s failure to improve software video tutorials. British Journal of Educational Technology, 52(1), 318–336. https://doi.org/10.1111/bjet.13000CrossRef

Gola, G., Angioletti, L., Cassioli, F., & Balconi, M. (2022). The Teaching Brain: Beyond the Science of Teaching and Educational Neuroscience. Frontiers in Psychology, 13, 823832. https://doi.org/10.3389/fpsyg.2022.823832CrossRef

Guo, X., Zhu, T., Wu, C., Bao, Z., & Liu, Y. (2022). Emotional activity is negatively associated with cognitive load in multimedia learning: a case study with EEG signals. Frontiers in Psychology, 13, 889427. https://doi.org/10.3389/fpsyg.2022.889427CrossRef

Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. Paper presented at the First ACM conference on Learning (pp:41–50). Atlanta Georgia.

Gupta, S., & Kumar, P. (2021). Attention Recognition System in Online Learning Platform Using EEG Signals. In P. K. Bora, S. Nandi, & S. Laskar (Eds.), Emerging Technologies for Smart Cities. Lecture Notes in Electrical Engineering (pp. 139–152). Springer Singapore. https://doi.org/10.1007/978-981-16-1550-4_15

Hari, R., & Kujala, M. V. (2009). Brain basis of human social interaction: from concepts to brain imaging. Physiological Reviews, 89(2), 453–479. https://doi.org/10.1152/physrev.00041.2007CrossRef

Hershey, T., Campbell, M. C., Videen, T. O., Lugar, H. M., Weaver, P. M., Hartlein, J., Karimi, M., Tabbal, S. D., & Perlmutter, J. S. (2010). Mapping Go–No-Go performance within the subthalamic nucleus region. Brain, 133(12), 3625–3634. https://doi.org/10.1093/brain/awq256CrossRef

Hew, K. F., & Lo, C. K. (2020). Comparing video styles and study strategies during video-recorded lectures: Effects on secondary school mathematics students’ preference and learning. Interactive Learning Environments, 28(7), 847–864. https://doi.org/10.1080/10494820.2018.1545671CrossRef

Holmes, E. A., O’Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Cohen Silver, R., Everall, I., Ford, T., John, A., Kabir, T., King, K., Madan, I., Michie, S., Przybylski, A. K., Shafran, R., Sweeney, A., … Bullmore, E. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. The Lancet Psychiatry, 7(6), 547–560. https://doi.org/10.1016/S2215-0366(20)30168-1CrossRef

Hong, J., Pi, Z., & Yang, J. (2018). Learning declarative and procedural knowledge via video lectures: Cognitive load and learning effectiveness. Innovations in Education and Teaching International, 55(1), 74–81. https://doi.org/10.1080/14703297.2016.1237371CrossRef

Howard-Jones, P. A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., Laurillard, D., & Thomas, M. S. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 620–627. https://doi.org/10.1037/rev0000036CrossRef

Huberman, A. (2021). Optimize Your Learning & Creativity with Science-Based Tools. https://www.youtube.com/watch?v=uuP-1ioh4LY

Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks in online lecture videos. Paper presented at the First ACM conference on Learning (pp:31–40). Atlanta Georgia.

Kokoç, M., IIgaz, H., & Altun, A. (2020). Effects of sustained attention and video lecture types on learning performances. Educational Technology Research and Development, 68(6), 3015–3039. https://doi.org/10.1007/s11423-020-09829-7CrossRef

Kolås, L. (2015). Application of interactive videos in education. Paper presented at the 2015 International Conference on Information Technology Based Higher Education and Training (ITHET) (pp:1–6).IEEE. https://doi.org/10.1109/ITHET.2015.7218037.

Kostorz, K., Flanagin, V. L., & Glasauer, S. (2020). Synchronization between instructor and observer when learning a complex bimanual skill. NeuroImage, 216, 116659. https://doi.org/10.1016/j.neuroimage.2020.116659CrossRef

Kostorz, K., Flanagin, V. L., & Glasauer, S. (2022). Intersubject synchrony of viewers during naturalistic observational self-learning of a complex bimanual task. Neuroimage Reports, 2(2), 100084. https://doi.org/10.1016/j.ynirp.2022.100084CrossRef

Lackmann, S., Léger, P., Charland, P., Aubé, C., & Talbot, J. (2021). The Influence of Video Format on Engagement and Performance in Online Learning. Brain Sciences, 11(2), 128. https://doi.org/10.3390/brainsci11020128CrossRef

Lang, Y., Xie, K., Gong, S., Wang, Y., & Cao, Y. (2022). The impact of emotional feedback and elaborated feedback of a pedagogical agent on multimedia learning. Frontiers in Psychology, 13, 810194. https://doi.org/10.3389/fpsyg.2022.810194CrossRef

Li, W., Wang, F., & Mayer, R. E. (2023). How to guide learners’ processing of multimedia lessons with pedagogical agents. Learning and Instruction, 84, 101729. https://doi.org/10.1016/j.learninstruc.2022.101729CrossRef

Li, W., Wang, F., Mayer, R., & Liu, T. (2022). Animated pedagogical agents enhance learning outcomes and brain activity during learning. Journal of Computer Assisted Learning, 38. https://doi.org/10.1111/jcal.12634

Lim, J. R. N., Rosenthal, S., Sim, Y. J. M., Lim, Z., & Oh, K. R. (2021). Making online learning more satisfying: The effects of online-learning self-efficacy, social presence and content structure. Technology, Pedagogy and Education, 30(4), 543–556. https://doi.org/10.1080/1475939X.2021.1934102CrossRef

Lin, Y., & Chen, C. (2019). Improving effectiveness of learners’ review of video lectures by using an attention-based video lecture review mechanism based on brainwave signals. Interactive Learning Environments, 27(1), 86–102. https://doi.org/10.1080/10494820.2018.1451899CrossRef

Lin, F., & Kao, C. (2018). Mental effort detection using EEG data in E-learning contexts. Computers and Education, 122, 63–79. https://doi.org/10.1016/j.compedu.2018.03.020CrossRef

Lin, X., Tang, W., Ma, W., Liu, Y., & Ding, F. (2023). The impact of media diversity and cognitive style on learning experience in programming video lecture: A brainwave analysis. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11608-9CrossRef

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.2019.100863CrossRef

Liu, Y., Ma, W. F., Guo, X., Lin, X. F., Wu, C. N., & Zhu, T. S. (2021). impacts of color coding on programming learning in multimedia learning: moving toward a multimodal methodology. Frontiers in Psychology, 12, 773328. https://doi.org/10.3389/fpsyg.2021.773328CrossRef

Matusz, P. J., Dikker, S., Huth, A. G., & Perrodin, C. (2019). Are We Ready for Real-world Neuroscience? Journal of Cognitive Neuroscience, 31(3), 327–338. https://doi.org/10.1162/jocn_e_01276CrossRef

Mayer, R. E. (2017). How can brain research inform academic learning and instruction? Educational Psychology Review, 29(4), 835–846. https://doi.org/10.1007/s10648-016-9391-1CrossRef

Mayer, R. E. (2021). Evidence-Based Principles for How to Design Effective Instructional Videos. Journal of Applied Research in Memory and Cognition, 10(2), 229–240. https://doi.org/10.1016/j.jarmac.2021.03.007CrossRef

Mayer, R. E., Fiorella, L., & Stull, A. (2020). Five ways to increase the effectiveness of instructional video. Educational Technology Research and Development, 68(3), 837–852. https://doi.org/10.1007/s11423-020-09749-6CrossRef

Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.

Merkt, M., Lux, S., Hoogerheide, V., van Gog, T., & Schwan, S. (2020). A change of scenery: Does the setting of an instructional video affect learning? Journal of Educational Psychology, 112(6), 1273–1283. https://doi.org/10.1037/edu0000414CrossRef

Meshulam, M., Hasenfratz, L., Hillman, H., Liu, Y., Nguyen, M., Norman, K. A., & Hasson, U. (2021). Neural alignment predicts learning outcomes in students taking an introduction to computer science course. Nature Communications, 12(1), 1922. https://doi.org/10.1038/s41467-021-22202-3CrossRef

Meyerhoff, H. S., Merkt, M., Schröpel, C., & Meder, A. (2022). Medical education videos as a tool for rehearsal: Efficiency and the cases of background music and difficulty. Instructional Science, 50(6), 879–901. https://doi.org/10.1007/s11251-022-09595-4CrossRef

Murphy, D. H., Hoover, K. M., Agadzhanyan, K., Kuehn, J. C., & Castel, A. D. (2022). Learning in double time: The effect of lecture video speed on immediate and delayed comprehension. Applied Cognitive Psychology, 36(1), 69–82. https://doi.org/10.1002/acp.3899CrossRef

Negi, S., & Mitra, R. (2022). Native language subtitling of educational videos: A multimodal analysis with eye tracking, EEG and self-reports. British Journal of Educational Technology, 53(6), 1793–1816. https://doi.org/10.1111/bjet.13214CrossRef

Nguyen, M., Chang, A., Micciche, E., Meshulam, M., Nastase, S. A., & Hasson, U. (2022). Teacher-student neural coupling during teaching and learning. Social Cognitive and Affective Neuroscience, 17(4), 367–376. https://doi.org/10.1093/scan/nsab103CrossRef

Noetel, M., Griffith, S., Delaney, O., Harris, N. R., Sanders, T., Parker, P., Del Pozo Cruz, B., & Lonsdale, C. (2021). Multimedia design for learning: an overview of reviews with meta-meta-analysis. Review of Educational Research, 92(3), 413–454. https://doi.org/10.3102/00346543211052329CrossRef

Nozawa, T., Sakaki, K., Ikeda, S., Jeong, H., Yamazaki, S., Dos Santos Kawata, K. H., Dos Santos Kawata, N. Y., Sasaki, Y., Kulason, K., & Hirano, K. (2019). Prior physical synchrony enhances rapport and inter-brain synchronization during subsequent educational communication. Scientific Reports, 9(1), 1–13. https://doi.org/10.1038/s41598-019-49257-zCrossRef

Oku, A. Y. A., & Sato, J. R. (2021). Predicting student performance using machine learning in fNIRS data. Frontiers in Human Neuroscience, 15, 622224. https://doi.org/10.3389/fnhum.2021.622224CrossRef

Pan, Y., Dikker, S., Goldstein, P., Zhu, Y., Yang, C., & Hu, Y. (2020). Instructor-learner brain coupling discriminates between instructional approaches and predicts learning. NeuroImage, 211(2020), 116657. https://doi.org/10.1016/j.neuroimage.2020.116657CrossRef

Pan, Y., Novembre, G., Song, B., Zhu, Y., & Hu, Y. (2021). Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony. Social Cognitive and Affective Neuroscience, 16(1–2), 210–221. https://doi.org/10.1093/scan/nsaa080CrossRef

Pi, Z., Chen, M., Zhu, F., Yang, J., & Hu, W. (2022a). Modulation of instructor’s eye gaze by facial expression in video lectures. Innovations in Education and Teaching International, 59(1), 15–23. https://doi.org/10.1080/14703297.2020.1788410

Pi, Z., Deng, L., Wang, X., Guo, P., Xu, T., & Zhou, Y. (2022b). The influences of a virtual instructor’s voice and appearance on learning from video lectures. Journal of Computer Assisted Learning, 38(6), 1703–1713. https://doi.org/10.1111/jcal.12704

Pi, Z., Zhang, Y., Liu, C., Zhou, W., & Yang, J. (2022c). Generative learning supports learning from video lectures: Evidence from an EEG study. Instructional Science. https://doi.org/10.1007/s11251-022-09602-8

Pi, Z., Zhang, Y., Yu, Q., Zhang, Y., Yang, J., & Zhao, Q. (2022d). Neural oscillations and learning performance vary with an instructor’s gestures and visual materials in video lectures. British Journal of Educational Technology, 53(1), 93–113. https://doi.org/10.1111/bjet.13154

Pi, Z., Zhu, F., Zhang, Y., Chen, L., & Yang, J. (2022e). Complexity of visual learning material moderates the effects of instructor’s beat gestures and head nods in video lectures. Learning and Instruction, 77, 101520. https://doi.org/10.1016/j.learninstruc.2021.101520

Pi, Z., Zhang, Y., Zhou, W., Xu, K., Chen, Y., Yang, J., & Zhao, Q. (2021a). 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. https://doi.org/10.1111/bjet.13048

Pi, Z., Zhang, Y., Zhu, F., Chen, L., Guo, X., & Yang, J. (2021b). The mutual influence of an instructor's eye gaze and facial expression in video lectures. Interactive Learning Environments, 1–18. https://doi.org/10.1080/10494820.2021.1940213

Pi, Z., Zhu, F., Zhang, Y., & Yang, J. (2021c). An instructor's beat gestures facilitate second language vocabulary learning from instructional videos: Behavioral and neural evidence. Language Teaching Research, 13621688211039023. https://doi.org/10.1177/13621688211039023

Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. W. (2020). The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1), 5–29. https://doi.org/10.1111/nyas.13948CrossRef

Polat, H. (2020). Investigating the use of text positions on videos: an eye movement study. Contemporary Educational Technology, 12(1), ep262. https://doi.org/10.30935/cedtech/7628CrossRef

Pope, A. T., Bogart, E. H., & Bartolome, D. S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1), 187–195. https://doi.org/10.1016/0301-0511(95)05116-3CrossRef

Poulsen, A. T., Kamronn, S., Dmochowski, J., Parra, L. C., & Hansen, L. K. (2017). EEG in the classroom: Synchronised neural recordings during video presentation. Scientific Reports, 7, 43916. https://doi.org/10.1038/srep43916CrossRef

Qian, H., Li, H., Tang, S., Wang, J., Liu, Q., & Chen, G. (2022). How teacher enthusiasm affects students’ learning of chemistry declarative knowledge in video lectures. Chemistry Education Research and Practice. https://doi.org/10.1039/D2RP00095DCrossRef

Quaresima, V., & Ferrari, M. (2016). Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: A concise review. Organizational Research Methods, 22(1), 46–68. https://doi.org/10.1177/1094428116658959CrossRef

Redcay, E., & Schilbach, L. (2019). Using second-person neuroscience to elucidate the mechanisms of social interaction. Nature Reviews Neuroscience, 20(8), 495–505. https://doi.org/10.1038/s41583-019-0179-4CrossRef

Rickley, M., & Kemp, P. (2021). Effects of video lecture design and production quality on student outcomes: a quasi-experiment exploiting change in online course development principles. Electronic Journal of e-Learning, 19(3), 170–185. https://doi.org/10.34190/ejel.19.3.2297CrossRef

Rodriguez, V. (2013). The human nervous system: A framework for teaching and the teaching brain. Mind, Brain, and Education, 7(1), 2–12. https://doi.org/10.1111/mbe.12000CrossRef

Rodriguez, V., & Lynneth Solis, S. (2013). Teachers’ awareness of the learner-teacher interaction: preliminary communication of a study investigating the teaching brain. Mind, Brain, and Education, 7(3), 161–169. https://doi.org/10.1111/mbe.12023CrossRef

Sałabun, W. (2014). Processing and spectral analysis of the raw EEG signal from the MindWave. Przeglad Elektrotechniczny, 90, 169–174. https://doi.org/10.12915/pe.2014.02.44CrossRef

Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T., & Vogeley, K. (2013). Toward a second-person neuroscience. Behavioral and Brain Sciences, 36(4), 393–414. https://doi.org/10.1017/S0140525X12000660CrossRef

Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Keysers, C. (2010). Mapping the information flow from one brain to another during gestural communication. Proceedings of the National Academy of Sciences, 107(20), 9388–9393. https://doi.org/10.1073/pnas.1001791107CrossRef

Schneider, S., Krieglstein, F., Beege, M., & Rey, G. D. (2022). The impact of video lecturers’ nonverbal communication on learning – An experiment on gestures and facial expressions of pedagogical agents. Computers and Education, 176, 104350. https://doi.org/10.1016/j.compedu.2021.104350CrossRef

Schrader, C., Seufert, T., & Zander, S. (2021). Learning From Instructional Videos: Learner Gender Does Matter; Speaker Gender Does Not. Frontiers in Psychology, 12, 1593. https://doi.org/10.3389/fpsyg.2021.655720CrossRef

Schwartz, A. M., Wilson, J. M., Boden, S. D., Moore, T. J., & Fletcher, N. D. (2020). Managing resident workforce and education during the covid-19 pandemic: evolving strategies and lessons learned. JBJS Open Access, 5, e0045. https://doi.org/10.2106/JBJS.OA.20.00045CrossRef

Serrhini, M., & Dargham, A. (2017). Toward Incorporating Bio-signals in Online Education Case of Assessing Student Attention with BCI. In Á. Rocha, M. Serrhini, & C. Felgueiras (Eds.), Europe and MENA Cooperation Advances in Information and Communication Technologies. Advances in Intelligent Systems and Computing (pp. 135–146). Springer International Publishing.

Sezer, A., Nel, Y., Sekin, A. A., & Ulunar, U. (2015). An Investigation of University Students' Attention Levels in Real Classroom Settings with NeuroSky's MindWave Mobile (EEG) Device. Paper presented at the International Educational Technology Conference–IETC 2015 (pp:88–101). Istanbul. IETC.

Shamay-Tsoory, S. G., & Mendelsohn, A. (2019). Real-Life Neuroscience: An Ecological Approach to Brain and Behavior Research. Perspectives on Psychological Science, 14(5), 841–859. https://doi.org/10.1177/1745691619856350CrossRef

Sondermann, C., & Merkt, M. (2023). Like it or learn from it: Effects of talking heads in educational videos. Computers and Education, 193, 104675. https://doi.org/10.1016/j.compedu.2022.104675CrossRef

Srivastava, N., Nawaz, S., Lodge, J. M., Velloso, E., Erfani, S., & Bailey, J. (2020). Exploring the usage of thermal imaging for understanding video lecture designs and students' experiences. Paper presented at the Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp:250–259). New York, NY, USA. Association for Computing Machinery. https://doi.org/10.1145/3375462.3375514.

Stull, A. T., Fiorella, L., & Mayer, R. E. (2018). An eye-tracking analysis of instructor presence in video lectures. Computers in Human Behavior, 88, 263–272. https://doi.org/10.1016/j.chb.2018.07.019CrossRef

Stull, A. T., Fiorella, L., & Mayer, R. E. (2021). The case for embodied instruction: The instructor as a source of attentional and social cues in video lectures. Journal of Educational Psychology, 113(7), 1441–1453. https://doi.org/10.1037/edu0000650CrossRef

Sun, B., Xiao, W., Feng, X., Shao, Y., Zhang, W., & Li, W. (2020). Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students. Brain and Cognition, 139, 105513. https://doi.org/10.1016/j.bandc.2019.105513CrossRef

Takeuchi, N., Mori, T., Suzukamo, Y., & Izumi, S. (2017). Integration of teaching processes and learning assessment in the prefrontal cortex during a video game teaching–learning task. Frontiers in Psychology, 7, 2052. https://doi.org/10.3389/fpsyg.2016.02052CrossRef

Tarchi, C., Zaccoletti, S., & Mason, L. (2021). Learning from text, video, or subtitles: A comparative analysis. Computers and Education, 160, 104034. https://doi.org/10.1016/j.compedu.2020.104034CrossRef

Tian, Y., Qi, Z., Huang, X., Xiang, H., & Wang, Y. (2021). Cognitive neural mechanism of social cues promoting online learning (in Chinese). E-education Research, 42(02), 63–69. https://doi.org/10.13811/j.cnki.eer.2021.02.009CrossRef

van der Meij, H., & Draijer, E. (2021). Design Principles for Multimedia Presentations: A Comparison between a Whiteboard Animation and a PowerPoint Slideshow Presentation. Journal of Educational Multimedia and Hypermedia, 30(4), 393–418.

van Wermeskerken, M., Ravensbergen, S., & van Gog, T. (2018). Effects of instructor presence in video modeling examples on attention and learning. Computers in Human Behavior, 89, 430–438. https://doi.org/10.1016/j.chb.2017.11.038CrossRef

Wang, G., & Fu, G. (2021). The influence of knowledge types, presentation methods and learning styles on college students’ online learning (in Chinese). Modern Educational Technology, 9(31), 45–54. https://doi.org/10.3969/j.issn.1009-8097.2021.09.006CrossRef

Wang, J., Antonenko, P., Keil, A., & Dawson, K. (2020a). Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video. Mind, Brain, and Education, 14(3), 279–291. https://doi.org/10.1111/mbe.12239

Wang, J., Antonenko, P., & Dawson, K. (2020b). Does visual attention to the instructor in online video affect learning and learner perceptions? An Eye-Tracking Analysis. Computers and Education, 146, 103779. https://doi.org/10.1016/j.compedu.2019.103779

Wang, X., Han, M., Gao, Z., & Wang, Z. (2020c). Research on the mechanism and optimization strategy of visual and auditory emotional design in instructional videos (in Chinese). Journal of Distance Education, 38(06), 50–61. https://doi.org/10.15881/j.cnki.cn33-1304/g4.2020.06.006

Wang, P., Chiu, M., & Lee, Y. (2021). Effects of video lecture presentation style and questioning strategy on learner flow experience. Innovations in Education and Teaching International, 58(4), 473–483. https://doi.org/10.1080/14703297.2020.1754272CrossRef

Wang, X., Gao, Z., Zhang, L., & Wang, Z. (2022). Research on mechanisms and strategies of value induction to promote video learning: analysis based on multimodal data (in Chinese). E-education Research, 43(2), 56–63. https://doi.org/10.13811/j.cnki.eer.2022.02.008CrossRef

Wang, Y., Wang, F., Mayer, R., Hu, X., & Gong, S. (2023). Benefits of prompting students to generate summaries during pauses in segmented multimedia lessons. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12797CrossRef

Watanabe, K. (2013). Teaching as a Dynamic Phenomenon with Interpersonal Interactions. Mind, Brain, and Education, 7(2), 91–100. https://doi.org/10.1111/mbe.12011CrossRef

Wong, M., Castro-Alonso, J. C., Ayres, P., & Paas, F. (2018). Investigating gender and spatial measurements in instructional animation research. Computers in Human Behavior, 89, 446–456. https://doi.org/10.1016/j.chb.2018.02.017CrossRef

Yang, J., Ai, S., Pi, Z., & Yu, Q. (2021a). The Influence Teacher’s Presence in the Teaching Videos on Teachers——Empirical Analysis based on the Controlled Experiment and Interview (in Chinese). Modern Educational Technology, 31(01), 54–61. https://doi.org/10.3969/j.issn.1009-8097.2021.01.008

Yang, J., Zhang, Y., Pi, Z., & Xie, Y. (2021b). Students’ achievement motivation moderates the effects of interpolated pre-questions on attention and learning from video lectures. Learning and Individual Differences, 91, 102055. https://doi.org/10.1016/j.lindif.2021.102055

Yousef, A. M. F., Chatti, M. A., & Schroeder, U. (2014). The state of video-based learning: A review and future perspectives. International Journal on Advances in Life Sciences, 6(3), 122–135.

Yuan, M., Zeng, J., Wang, A., & Shang, J. (2021). Would it be better if instructors technically adjust their image or voice in online courses? Impact of the way of instructor presence on online learning. Frontiers in Psychology, 12, 746857. https://doi.org/10.3389/fpsyg.2021.746857CrossRef

Zhang, Y., & Yang, J. (2022). Exploring Gender Differences in the Instructor Presence Effect in Video Lectures: An Eye-Tracking Study. Brain Sciences, 12, 946. https://doi.org/10.3390/brainsci12070946CrossRef

Zhang, L., Xu, X., Li, Z., Chen, L., & Feng, L. (2022). Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis. Frontiers in Psychology, 13, 835147. https://doi.org/10.3389/fpsyg.2022.835147CrossRef

Zhao, X., Yang, X., & Li, X. (2017). The Brainwave Experimental Study of the Effect of Subtitle’s Presentation Modes of M-Learning Courseware on the Attention (in Chinese). Modern Distance Education Research, 01, 95–104. https://doi.org/10.3969/j.issn.1009-5195.2017.01.012CrossRef

Zheng, L., Chen, C., Liu, W., Long, Y., Zhao, H., Bai, X., Zhang, Z., Han, Z., Liu, L., & Guo, T. (2018). Enhancement of teaching outcome through neural prediction of the students’ knowledge state. Human Brain Mapping, 39(7), 3046–3057. https://doi.org/10.1002/hbm.24059CrossRef

Zheng, L., Liu, W., Long, Y., Zhai, Y., Zhao, H., Bai, X., Zhou, S., Li, K., Zhang, H., & Liu, L. (2020). Affiliative bonding between teachers and students through interpersonal synchronisation in brain activity. Social Cognitive and Affective Neuroscience, 15(1), 97–109. https://doi.org/10.1093/scan/nsaa016CrossRef

Zhou, J., Gao, Y., Zhang, Z., & Chen, D. (2022). Neural Mechanism and Visual Analysis of Interactive Video Teaching (in Chinese). Modern Educational Technology, 32(9), 19–30. https://doi.org/10.3969/j.issn.1009-8097.2022.09.002CrossRef

Zhu, Y., Pan, Y., & Hu, Y. (2019). Learning Desire Is Predicted by Similar Neural Processing of Naturalistic Educational Materials. eNeuro, 6(5), ENEURO.0083–19.2019. https://doi.org/10.1523/ENEURO.0083-19.2019

Title: Video Education through the Lens of Educational Neuroscience: A Concise Review
Authors: Zhicong Zhang
Yuting Gao
Yafeng Pan
Jiaxian Zhou
Publication date: 15-03-2024
Publisher: Springer US
Published in: TechTrends / Issue 3/2024
Print ISSN: 8756-3894
Electronic ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-024-00946-1

ALEKS in High School Mathematics Classrooms: Exploring Teachers’ Perceptions and Use of this Tool

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Column: Rethinking Creativity and Technology in Education

Should Graduate Students be Peer Reviewers in Academic Journals?

Column: Editor’s Notes

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Valuing Diverse Learning Ecologies and Climates: Through a strength-based lens

Column: Special Section Editor's Notes

Strength-Based Learning: An Autoethnography of an Introductory Instructional Design Graduate Course

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Original Paper

Springer Professional

Video Education through the Lens of Educational Neuroscience: A Concise Review

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ALEKS in High School Mathematics Classrooms: Exploring Teachers’ Perceptions and Use of this Tool

Creative Dialogue with Generative AI: Exploring the Possible with Ron Beghetto

Should Graduate Students be Peer Reviewers in Academic Journals?

An Exploration of How Students Prioritize Help-Seeking Sources in Online Learning Environments

Valuing Diverse Learning Ecologies and Climates: Through a strength-based lens

Strength-Based Learning: An Autoethnography of an Introductory Instructional Design Graduate Course