Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards “naturalistic” approaches, with keywords such as “face-to-face” and “real-world” gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.
Author contributions
Q.L. and Y.P. conceived the idea. Q.L. conducted the literature search, analyzed the data, and wrote the initial manuscript. Y.P. supervised the project and reviewed/edited the manuscript.
Conflict of interest
None declared.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 62207025, 62337001), the Zhejiang Provincial Natural Science Foundation of China (No. LMS25C090002), and the Fundamental Research Funds for the Central Universities to Y.P.
| [1] |
Achuthan K, Nair VK, Kowalski R, et al. (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: bibliometrics and science mapping analysis. Comput Hum Behav. 140:107566.
|
| [2] |
Ackermann S, Hartmann F, Papassotiropoulos A, et al. (2015) No associations between interindividual differences in sleep parameters and episodic memory consolidation. Sleep. 38:951-9.
|
| [3] |
Alonso A, McDorman SA, Romeo RR. (2024) How parent-child brain-to-brain synchrony can inform the study of child development. Child Dev Perspectives. 18:26-35.
|
| [4] |
Alqahtani F, Katsigiannis S, Ramzan N. (2020) Using wearable physiological sensors for affect-aware intelligent tutoring systems. IEEE Sensors J. 21:3366-78.
|
| [5] |
Antle AN, Chesick L, Sridharan SK, et al. (2018) East meets west: a mobile brain-computer system that helps children living in poverty learn to self-regulate. Pers Ubiquit Comput. 22:839-66.
|
| [6] |
Antonenko PD, Niederhauser DS. (2010) The influence of leads on cognitive load and learning in a hypertext environment. Comput Hum Behav. 26:140-50.
|
| [7] |
Arici F, Yildirim P, Caliklar Ş, et al. (2019) Research trends in the use of augmented reality in science education: content and bibliometric mapping analysis. Comput Educ. 142:103647.
|
| [8] |
Ayaz H, Shewokis PA, Curtin A, et al. (2011) Using MazeSuite and functional near infrared spectroscopy to study learning in spatial navigation. J Vis Exp. 56:e3443.
|
| [9] |
Babiloni F, Astolfi L. (2014) Social neuroscience and hyperscanning techniques: past, present and future. Neurosci Biobehav Rev. 44:76-93.
|
| [10] |
Baceviciute S, Lucas G, Terkildsen T, et al. (2022) Investigating the redundancy principle in immersive virtual reality environments: an eye-tracking and EEG study. J Comput Assist Lear. 38:120-36.
|
| [11] |
Baddeley A. (2003) Working memory: looking back and looking forward. Nat Rev Neurosci. 4:829-39.
|
| [12] |
Balconi M, Angioletti L, Cassioli F. (2023) Hyperscanning EEG paradigm applied to remote vs. face-to-face learning in managerial contexts: which is better?. Brain Sci. 13:356.
|
| [13] |
Berka C, Levendowski DJ, Cvetinovic MM, et al. (2004) Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int J Hum-Comput Interact. 17:151-70.
|
| [14] |
Berka C, Levendowski DJ, Lumicao MN, et al. (2007) EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat Space Environ Med. 78:B231-44.
|
| [15] |
Bevilacqua D, Davidesco I, Wan L, et al. (2019) Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: evidence from a real-world classroom electroencephalography study. J Cogn Neurosci. 31:401-11.
|
| [16] |
Brockington G, Balardin JB, Zimeo Morais GA, et al. (2018) From the laboratory to the classroom: the potential of functional near-infrared spectroscopy in educational neuroscience. Front Psychol. 9:1840.
|
| [17] |
Brügger A, Richter KF, Fabrikant SI. (2019) How does navigation system behavior influence human behavior?. Cogn Res. 4:5.
|
| [18] |
Chen J, Qian P, Gao X, et al. (2023) Inter-brain coupling reflects disciplinary differences in real-world classroom learning. Npj Sci Learn. 8:11.
|
| [19] |
Cheng B, Lin E, Wunderlich A, et al. (2023) Using spontaneous eye blink-related brain activity to investigate cognitive load during mobile map-assisted navigation. Front Neurosci. 17:1024583.
|
| [20] |
Chuang HH, Liu HC. (2012) Effects of different multimedia presentations on viewers’ information-processing activities measured by eye-tracking technology. J Sci Educ Technol. 21:276-86.
|
| [21] |
Cortina KS, Miller KF, Mckenzie R, et al. (2015) Where low and high inference data converge: validation of CLASS assessment of mathematics instruction using mobile eye tracking with expert and novice teachers. Int J Sci Math Educ. 13:389-403.
|
| [22] |
Coskun A, Cagiltay K. (2021) Investigation of classroom management skills by using eye-tracking technology. Educ Inf Technol. 26:2501-22.
|
| [23] |
Dahlstrom‐Hakki I, Asbell‐Clarke J, Rowe E (2019) Showing is knowing: the potential and challenges of using neurocognitive measures of implicit learning in the classroom. Mind Brain Educ. 13:30-40.
|
| [24] |
Davidesco I, Matuk C, Bevilacqua D, et al. (2021) Neuroscience research in the classroom: portable brain technologies in education research. Educ Res. 50:649-56.
|
| [25] |
Dikker S, Haegens S, Bevilacqua D, et al. (2020) Morning brain: real-world neural evidence that high school class times matter. Soc Cogn Affect Neurosci. 15:1193-202.
|
| [26] |
Dikker S, Wan L, Davidesco I, et al. (2017) Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Curr Biol. 27:1375-80.
|
| [27] |
Donthu N, Kumar S, Mukherjee D, et al. (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Business Res. 133:285-96.
|
| [28] |
Feiler JB, Stabio ME. (2018) Three pillars of educational neuroscience from three decades of literature. Trends Neurosci Educ. 13:17-25.
|
| [29] |
Feng X, Xu X, Meng Z, et al. (2025) A rapid cortical learning process supporting students’ Knowledge construction during real classroom teaching. Adv Sci. 12, 2416610.
|
| [30] |
Filho E, Husselman T-A, Zugic L, et al. (2022) Performance gains in an open skill video-game task: the role of neural efficiency and neural proficiency. Appl Psychophysiol Biofeedback. 47:239-51.
|
| [31] |
Fischer KW, Goswami U, Geake J. (2010) The future of educational neuroscience. Mind Brain Educ. 4:68-80.
|
| [32] |
Gao W, Wei T, Huang H, et al. (2022) Toward a systematic survey on wearable computing for education applications. IEEE Internet Things J. 9:12901-15.
|
| [33] |
Goldberg P, Schwerter J, Seidel T, et al. (2021) How does learners’ behavior attract preservice teachers’ attention during teaching?. Teach Teach Educ. 97:103213.
|
| [34] |
Guo J, Wan B, Wu H, et al. (2022) A virtual reality and online learning immersion experience evaluation model based on SVM and wearable recordings. Electronics. 11:1429.
|
| [35] |
Haar S, Faisal AA. (2020) Brain activity reveals multiple motor-learning mechanisms in a real-world task. Front Hum Neurosci. 14:354.
|
| [36] |
Haataja E, Garcia Moreno-Esteva E, Salonen V, et al. (2019) Teacher's visual attention when scaffolding collaborative mathematical problem solving. Teach Teach Educ. 86:102877.
|
| [37] |
Harley JM, Poitras EG, Jarrell A, et al. (2016) Comparing virtual and location-based augmented reality mobile learning: emotions and learning outcomes. Education Tech Res Dev. 64:359-88.
|
| [38] |
Hassan-Montero Y, De-Moya-Anegón F, Guerrero-Bote VP. (2022) SCImago Graphica: a new tool for exploring and visually communicating data. Profesional Inf/Inf Prof. 31:5.
|
| [39] |
Holper L, Goldin AP, Shalóm DE, et al. (2013) The teaching and the learning brain: a cortical hemodynamic marker of teacher-student interactions in the socratic dialog. Int J Educ Res. 59:1-10.
|
| [40] |
Hood WW, Wilson CS. (2001) The literature of bibliometrics, scientometrics, and informetrics. Scientometrics. 52:291-314.
|
| [41] |
Hou L, Pan Y, Zhu JJ. (2021) Impact of scientific, economic, geopolitical, and cultural factors on international research collaboration. J Informetrics. 15:101194.
|
| [42] |
Janssen TWP, Grammer JK, Bleichner MG, et al. (2021) Opportunities and limitations of mobile neuroimaging technologies in educational neuroscience. Mind Brain Educ. 15:354-70.
|
| [43] |
Jasińska KK, Guei S. (2018) Neuroimaging field methods using functional near infrared spectroscopy (NIRS) neuroimaging to study global child development: rural sub-Saharan Africa. J Vis Exp. 132.57165
|
| [44] |
Kandemir H, Kose H. (2022) Development of adaptive human-computer interaction games to evaluate attention. Robotica. 40:56-76.
|
| [45] |
Kapaj A, Lanini-Maggi S, Hilton C, et al. (2023) How does the design of landmarks on a mobile map influence wayfinding experts’ spatial learning during a real-world navigation task?. Cartogr Geographic Inform Sci. 50:197-213.
|
| [46] |
Kaveh R, Schwendeman C, Pu L, et al. (2024) Wireless ear EEG to monitor drowsiness. Nat Commun. 15:6520.
|
| [47] |
Khan A, Goodell JW, Hassan MK, et al. (2022) A bibliometric review of finance bibliometric papers. Finance Res Lett. 47:102520.
|
| [48] |
Klimesch W, Sauseng P, Hanslmayr S. (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev. 53:63-88.
|
| [49] |
Krakauer JW, Hadjiosif AM, Xu J, et al. (2019) Motor learning. Compr Physiol. 9:613-63.
|
| [50] |
Krigolson OE, Williams CC, Norton A, et al. (2017) Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Front Neurosci. 11:109.
|
| [51] |
Laal M, Laal M. (2012) Collaborative learning: what is it?. Procedia—Social Behavior Sci. 31:491-5.
|
| [52] |
Lai M-L, Tsai M-J, Yang F-Y, et al. (2013) A review of using eye-tracking technology in exploring learning from 2000 to 2012. Edu Res Rev. 10:90-115.
|
| [53] |
Lau-Zhu A, Lau MP, McLoughlin G. (2019) Mobile EEG in research on neurodevelopmental disorders: opportunities and challenges. Dev Cogn Neurosci. 36:100635.
|
| [54] |
Li J, Antonenko PD, Wang J. (2019) Trends and issues in multimedia learning research in 1996-2016: a bibliometric analysis. Edu Res Rev. 28:100282.
|
| [55] |
Liu NH, Chiang CY, Chu HC. (2013) Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors. 13:10273-86.
|
| [56] |
Liu X, Ardakani SP. (2022) A machine learning enabled affective E-learning system model. Educ Inf Technol. 27:9913-34.
|
| [57] |
Lloyd-Fox S, Blasi A, Elwell CE. (2010) Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. Neurosci Biobehav Rev. 34:269-84.
|
| [58] |
MacCoun RJ. (1998) Biases in the interpretation and use of research results. Annu Rev Psychol. 49:259-87.
|
| [59] |
Mailhot T, Lavoie P, Maheu-Cadotte M-A, et al. (2018) Using a wireless electroencephalography device to evaluate e-health and e-learning interventions. Nurs Res. 67:43-8.
|
| [60] |
Maimon NB, Bez M, Drobot D, et al. (2022) Continuous monitoring of mental load during virtual simulator training for laparoscopic surgery reflects laparoscopic dexterity: a comparative study using a novel wireless device. Front Neurosci. 15:694010.
|
| [61] |
Matveeva N, Sterligov I, Lovakov A. (2022) International scientific collaboration of post-Soviet countries: a bibliometric analysis. Scientometrics. 127:1583-607.
|
| [62] |
Mesmoudi S, Hommet S, Peschanski D. (2020) Eye-tracking and learning experience: gaze trajectories to better understand the behavior of memorial visitors. JEMR. 13:2.
|
| [63] |
Nguyen JP, Shipley FB, Linder AN, et al. (2016) Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans. Proc Natl Acad Sci U S A. 113:E1074-81.
|
| [64] |
Page MJ, Mckenzie JE, Bossuyt PM, et al. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 372:n71.
|
| [65] |
Pan Y, Novembre G, Olsson A (2022) The interpersonal neuroscience of social learning. Perspect Psychol Sci. 17:680-95.
|
| [66] |
Pan Y, Novembre G, Song B, et al. (2018) Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song. Neuroimage. 183:280-90.
|
| [67] |
Perpetuini D, Russo EF, Cardone D, et al. (2022) Identification of functional cortical plasticity in children with cerebral palsy associated to robotic-assisted gait training: an fNIRS study. JCM. 11:6790.
|
| [68] |
Pessoa L. (2008) On the relationship between emotion and cognition. Nat Rev Neurosci. 9:148-58.
|
| [69] |
Pierno AC, Becchio C, Turella L, et al. (2008) Observing social interactions: the effect of gaze. Social Neurosci. 3:51-9.
|
| [70] |
Polat H, Topuz AC, Yıldız M, et al. (2024) A bibliometric analysis of research on ChatGPT in education. IJTE. 7:59-85.
|
| [71] |
Pouta M, Lehtinen E, Palonen T (2021) Student teachers’ and experienced teachers’ professional vision of students’ understanding of the rational number concept. Educ Psychol Rev. 33:109-28.
|
| [72] |
Prieto LP, Sharma K, Kidzinski Ł, et al. (2018) Multimodal teaching analytics: automated extraction of orchestration graphs from wearable sensor data. Computer Assisted Learning. 34:193-203.
|
| [73] |
Rayner K. (1998) Eye movements in reading and information processing: 20 years of research. Psychol Bull. 124:372.
|
| [74] |
Salminen-Saari JFA, Garcia Moreno-Esteva E, Haataja E, et al. (2021) Phases of collaborative mathematical problem solving and joint attention: a case study utilizing mobile gaze tracking. ZDM Mathematics Edu. 53:771-84.
|
| [75] |
Schneider B, Sharma K, Cuendet S, et al. (2018) Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups. Int J Comput-Supp Collabor Learn. 13:241-61.
|
| [76] |
Schöbel S, Saqr M, Janson A. (2021) Two decades of game concepts in digital learning environments-A bibliometric study and research agenda. Comput Edu. 173:104296.
|
| [77] |
Scholkmann F, Kleiser S, Metz AJ, et al. (2014) A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage. 85:6-27.
|
| [78] |
Schroer SE, Yu C. (2023) Looking is not enough: multimodal attention supports the real-time learning of new words. Dev Sci. 26:e13290.
|
| [79] |
Seidel T, Stürmer K. (2014) Modeling and measuring the structure of professional vision in preservice teachers. Am Educ Res J. 51:739-71.
|
| [80] |
Shamay-Tsoory SG, Mendelsohn A. (2019) Real-life neuroscience: an ecological approach to brain and behavior research. Perspect Psychol Sci. 14:841-59.
|
| [81] |
Sharma K, Leftheriotis I, Giannakos M. (2020) Utilizing interactive surfaces to enhance learning, collaboration and engagement: insights from learners’ gaze and speech. Sensors. 20:1964.
|
| [82] |
Singh VK, Singh P, Karmakar M, et al. (2021) The journal coverage of Web of Science, Scopus and dimensions: a comparative analysis. Scientometrics. 126:5113-42.
|
| [83] |
Song Y, Chen X, Hao T, et al. (2019) Exploring two decades of research on classroom dialogue by using bibliometric analysis. Comput Edu. 137:12-31.
|
| [84] |
Stangl M, Maoz SL, Suthana N. (2023) Mobile cognition: imaging the human brain in the ‘real world.’. Nat Rev Neurosci. 24:347-62.
|
| [85] |
Sui J, Zhi D, Calhoun VD. (2023) Data-driven multimodal fusion: approaches and applications in psychiatric research. Psychoradiology, 3:kkad026. https://doi.org/10.1093/psyrad/kkad026.
|
| [86] |
Sumardani D, Lin CH. (2023) Cognitive processes during virtual reality learning: a study of brain wave. Educ Inf Technol. 28:14877-96.
|
| [87] |
Svoboda K, Li N. (2018) Neural mechanisms of movement planning: motor cortex and beyond. Curr Opin Neurobiol. 49:33-41.
|
| [88] |
Sweller J. (1988) Cognitive load during problem solving: effects on learning. Cogn Sci. 12:257-85.
|
| [89] |
Takeuchi N, Mori T, Suzukamo Y, et al. (2017) Integration of teaching processes and learning assessment in the prefrontal cortex during a video game teaching-learning task. Front Psychol. 7:2052.
|
| [90] |
Tan SJ, Wong JN, Teo WP. (2023) Is neuroimaging ready for the classroom? A systematic review of hyperscanning studies in learning. Neuroimage. 281:120367.
|
| [91] |
Van Eck N, Waltman L. (2010) Software survey: vOSviewer, a computer program for bibliometric mapping. Scientometrics. 84:523-38.
|
| [92] |
Vigliocco G, Convertino L, De Felice S, et al. (2024) Ecological brain: reframing the study of human behaviour and cognition. R Soc Open Sci. 11:240762.
|
| [93] |
Vošner HBž, Kokol P, Bobek S, et al. (2016) A bibliometric retrospective of the Journal Computers in Human Behavior (1991-2015). Comput Hum Behav. 65:46-58.
|
| [94] |
Wen D, Yuan J, Li J, et al. (2023) Design and test of spatial cognitive training and evaluation system based on virtual reality head-mounted display with EEG recording. IEEE Trans Neural Syst Rehabil Eng. 31:2705-14.
|
| [95] |
Whittier T, Willy RW, Sandri Heidner G, et al. (2020) The cognitive demands of gait retraining in runners: an EEG study. J Mot Behav. 52:360-71.
|
| [96] |
Xu J, Zhong B. (2018) Review on portable EEG technology in educational research. Comput Hum Behav. 81:340-9.
|
| [97] |
Yan W, Zheng K, Weng L, et al. (2020) Bibliometric evaluation of 2000-2019 publications on functional near-infrared spectroscopy. Neuroimage. 220:117121.
|
| [98] |
Yang LI, Huang J, Feng TI, et al. (2019) Gesture interaction in virtual reality. Virtual Reality & Intelligent Hardware. 1:84-112.
|
| [99] |
Yang X, Lin L, Cheng P-Y, et al. (2018) Examining creativity through a virtual reality support system. Edu Tech Res Dev. 66:1231-54.
|
| [100] |
Yoo G, Kim H, Hong S. (2023) Prediction of cognitive load from electroencephalography signals using long short-term memory network. Bioeng. 10:361.
|
| [101] |
Zhang J, Yu T, Wang M, et al. (2023) Clinical applications of functional near-infrared spectroscopy in the past decade: a bibliometric study. Appl Spectrosc Rev. 59:908-34.
|
| [102] |
Zhang Y, Hu Y, Ma F, et al. (2024) Interpersonal educational neuroscience: a scoping review of the literature. Edu Res Rev. 42:100593.
|
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