Yıl: 2022 Cilt: 10 Sayı: 19 Sayfa Aralığı: 184 - 201 Metin Dili: İngilizce DOI: 10.18009/jcer.1002588 İndeks Tarihi: 29-07-2022

The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study

Öz:
In the present study, 36 articles indexed in the Web of Science database were examined in order to reveal the current trend in scientific studies in the field of educational neuroscience. Therefore, the distribution of the articles was examined considering publication years, host journals, the most productive author(s), co-authorship, abstract keywords, collocated keywords, educational attainment of the samples, dependent variables, and the EEG devices used. The data were evaluated with descriptive and bibliometric analysis methods. The findings revealed that the publishing in the field gained an elevation in 2020; the papers were mostly published in Computers & Education; Mayer was the most productive author; Cheng, Lin, Yang, and Huang were those who produced the most collaborative studies in the field. In addition, it was found out that the keyword “cognitive load” was discussed more than the others; it was used with “attention” the most; studies were mostly carried out at university level; cognitive load and attention were the most examined dependent variables; the NeuroSky Mindwave was used in these articles the most. To sum, the present results have the potential to generate an overall perspective to educational neuroscience.
Anahtar Kelime: educational neuroscience neuroimaging

The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study

Öz:
In the present study, 36 articles indexed in the Web of Science database were examined in order to reveal the current trend in scientific studies in the field of educational neuroscience. Therefore, the distribution of the articles was examined considering publication years, host journals, the most productive author(s), co-authorship, abstract keywords, collocated keywords, educational attainment of the samples, dependent variables, and the EEG devices used. The data were evaluated with descriptive and bibliometric analysis methods. The findings revealed that the publishing in the field gained an elevation in 2020; the papers were mostly published in Computers & Education; Mayer was the most productive author; Cheng, Lin, Yang, and Huang were those who produced the most collaborative studies in the field. In addition, it was found out that the keyword “cognitive load” was discussed more than the others; it was used with “attention” the most; studies were mostly carried out at university level; cognitive load and attention were the most examined dependent variables; the NeuroSky Mindwave was used in these articles the most. To sum, the present results have the potential to generate an overall perspective to educational neuroscience.
Anahtar Kelime: EEG

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  • Ansari, D., Coch, D., & De Smedt, B. (2011). Connecting education and cognitive neuroscience: Where will the journey take us?. Educational philosophy and theory, 43(1), 37-42. https://doi.org/10.1111/j.1469-5812.2010.00705.x
  • Çakmak, E. K. (2007). The bottle neck in multimedia: Cognitive overload. Gazi University Gazi The Journal of Educational Faculty, 27(2), 1-24. http://www.gefad.gazi.edu.tr/en/pub/issue/6750/90766
  • Deryakulu, D., Atal, D., ve Sancar, R. (2019). Eğitimsel sinirbilim ve eğitim teknolojisi açısından doğurguları. (Editörler: A. İşman, H. F. Odabaşı ve B. Akkoyunlu). Eğitim Teknolojileri Okumaları 2019 içinde (ss. 331-348). Pegem Akademi.
  • Duman, B. (2015). Neden beyin temelli öğrenme? Pegem Akademi.
  • Dündar, S., (2013). The investigation of students' brain waves in the problem solving process (Unpublished Doctoral Dissertation). Gazi University.
  • Feiler, J. B., & Stabio, M. E. (2018). Three pillars of educational neuroscience from three decades of literature. Trends in neuroscience and education, 13, 17-25. https://doi.org/10.1016/j.tine.2018.11.001
  • Ferrari, M. (2011). What can neuroscience bring to education?. Educational Philosophy and Theory, 43(1), 31-36. https://doi.org/10.1111/j.1469-5812.2010.00704.x
  • Gürlen, E., Özdiyar, Ö., & Şen, Z. (2018). Social network analysis of academic studies on gifted people. Education and Science, 44(197), 185-208. http://dx.doi.org/10.15390/EB.2018.7735
  • İkiz, Y. D. (2021). An investigation of cognitive load effect using augmented reality glasses in an automotive assembly line (Unpublished Master Dissertation). Bursa Uludağ University.
  • Koçak, G. (2020). Reflections of brain researches to education: Toward education of future. Yeditepe University the Journal of Educational Faculty, 9(11), 1-16. https://dergipark.org.tr/tr/pub/edu7/issue/59006/731860
  • Morshad, S., Mazumder, M. R., & Ahmed, F. (2020, January). Analysis of brain wave data using Neurosky Mindwave Mobile II. In Proceedings of the International Conference on Computing Advancements (pp. 1-4). https://doi.org/10.1145/3377049.3377053
  • Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9-31. https://www.jstor.org/stable/41953635
  • Sweller, J., van Merrienboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review. 31, 261-292. https://doi.org/10.1007/s10648-019-09465-5
  • Tabakcıoğlu, M., Çizmeci, H., & Ayberkin, D. (2016). Neurosky EEG biosensor using in education. International Journal of Applied Mathematics Electronics and Computers, 76-78. https://doi.org/10.18100/ijamec.265371
  • Varma, S., McCandliss, B. D., & Schwartz, D. L. (2008). Scientific and pragmatic challenges for bridging education and neuroscience. Educational researcher, 37(3), 140-152. https://doi.org/10.3102/0013189X08317687
  • Yazgan, E., & Korurek, K. M., (1996). Tıp elektroniği. İ.T.Ü. Matbaası.
  • Baceviciute, S., Terkildsen, T., & Makransky, G. (2021). Remediating learning from nonimmersive to immersive media: Using EEG to investigate the effects of environmental embeddedness on reading in Virtual Reality. Computers & Education, 164, 15. https://doi.org/10.1016/j.compedu.2020.104122
  • Castro-Meneses, L. J., Kruger, J. L., & Doherty, S. (2020). Validating theta power as an objective measure of cognitive load in educational video. Etr&D-Educational Technology Research and Development, 68(1), 181-202. https://doi.org/10.1007/s11423- 019-09681-4
  • Chen, C. M., Wang, J. Y., & Yu, C. M. (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.12359
  • Cuesta-Cambra, U., Nino-Gonzalez, J. I., & Rodriguez-Terceno, J. (2017). The cognitive processing of an educational app with electroencephalogram and "Eye Tracking". Media Education Research Journal, 25(52), 41-50. https://doi.org/10.3916/c52-2017-04
  • 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
  • Delahunty, T., Seery, N., & Lynch, R. (2020). Exploring problem conceptualization and performance in STEM problem solving contexts. Instructional Science, 48(4), 395-425. https://doi.org/10.1007/s11251-020-09515-4
  • Eldenfria, A., & Al-Samarraie, H. (2019). The effectiveness of an online learning system based on aptitude scores: An effort to improve students' brain activation. Education and Information Technologies, 24(5), 2763-2777. https://doi.org/10.1007/s10639-019-09895-2
  • Ferreira, A. C., Santiago, M. A., & Ortega, M. D. L. (2021). Use of BCI devices in students for teacher assessment. Ried-Revista Iberoamericana De Educacion a Distancia, 24(1), 315-328. https://doi.org/10.5944/ried.24.1.27502
  • Hu, L. J., Xie, Y. J., & Sun, G. X. (2018). Computer-aided cognitive training based on electroencephalography-neurofeedback for English learning. Educational SciencesTheory & Practice, 18(5), 2593-2601. https://doi.org/10.12738/estp.2018.5.002
  • Huang, H. W., King, J. T., & Lee, C. L. (2020). The new science of learning: using the power and potential of the brain to inform digital learning. Educational Technology & Society, 23(4), 1-13. https://scholars.cityu.edu.hk/en/publications/publication(5299e970-a4d6- 4244-8000-e2535919ed45).html
  • Kadar, M., Borza, P. N., Romanca, M., Iordachescu, D., & Iordachescu, T. (2017). Smart testing environment for the evaluation of students' attention. Interaction Design and Architectures, 32, 205-217. http://www.mifav.uniroma2.it/inevent/events/idea2010/doc/32_13.pdf
  • Lambert, S., Dimitriadis, N., Taylor, M., & Venerucci, M. (2021). Understanding emotional empathy at postgraduate business programmes: what does the use of EEG reveal for future leaders? Higher Education Skills and Work-Based Learning, 12. https://doi.org/10.1108/heswbl-09-2020-0218
  • Lee, H. (2014). Measuring cognitive load with electroencephalography and self-report: focus on the effect of English-medium learning for Korean students. Educational Psychology, 34(7), 838-848. https://doi.org/10.1080/01443410.2013.860217
  • Lin, F. R., & Kao, C. M. (2018). Mental effort detection using EEG data in E-learning contexts. Computers & Education, 122, 63-79. https://doi.org/10.1016/j.compedu.2018.03.020
  • Liu, C. J., Huang, C. F., Liu, M. C., Chien, Y. C., Lai, C. H., & Huang, Y. M. (2015). Does gender influence emotions resulting from positive applause feedback in selfassessment testing? Evidence from Neuroscience. Educational Technology & Society, 18(1), 337-350. https://eric.ed.gov/?id=EJ1062511
  • Ma, M. Y., & Wei, C. C. (2016). A comparative study of children's concentration performance on picture books: age, gender, and media forms. Interactive Learning Environments, 24(8), 1922-1937. https://doi.org/10.1080/10494820.2015.1060505
  • 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. https://doi.org/10.1016/j.learninstruc.2017.12.007
  • Moreno, M., Schnabel, R., Lancia, G., & Woodruff, E. (2020). Between text and platforms: A case study on the real-time emotions & psychophysiological indicators of video gaming and academic engagement. Education and Information Technologies, 25(3), 2073- 2099. https://doi.org/10.1007/s10639-019-10031-3
  • Mutlu-Bayraktar, D., Ozel, P., Altindis, F., & Yilmaz, B. (2020). Relationship between objective and subjective cognitive load measurements in multimedia learning. Interactive Learning Environments, 13. https://doi.org/10.1080/10494820.2020.1833042
  • Pajk, T., Van Isacker, K., Abersek, B., & Flogie, A. (2021). STEM Education in eco-farming supported by ICT and mobile applications. Journal of Baltic Science Education, 20(2), 277-288. https://doi.org/10.33225/jbse/21.20.277
  • Parong, J., & Mayer, R. E. (2021a). Cognitive and affective processes for learning science in immersive virtual reality. Journal of Computer Assisted Learning, 37(1), 226-241. https://doi.org/10.1111/jcal.12482
  • Parong, J., & Mayer, R. E. (2021b). Learning about history in immersive virtual reality: does immersion facilitate learning? Etr&D-Educational Technology Research and Development, 19. https://doi.org/10.1007/s11423-021-09999-y
  • Pi, Z. L., Zhang, Y., Zhou, W. C., Xu, K., Chen, Y. R., Yang, J. M., & Zhao, Q. B. (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 e13048. https://doi.org/10.1111/bjet.13048
  • Robinson, K., Wehner, T., & Millward, H. (2019). Is the outcome of remote group work using text based CMC suboptimal? A psychobiological perspective. Computers & Education, 134, 108-118. https://doi.org/10.1016/j.compedu.2019.02.009
  • Sezer, A., Inel, Y., Seckin, A. C., & Ulucinar, U. (2017). The relationship between attention levels and class participation of first-year students in classroom teaching departments. International Journal of Instruction, 10(2), 55-68. https://doi.org/10.12973/iji.2017.1024a
  • Stockman, C. (2020). Can a technology teach meditation? Experiencing the EEG headband interaxon muse as a meditation guide. International Journal of Emerging Technologies in Learning, 15(8), 83-99. https://doi.org/10.3991/ijet.v15i08.12415
  • Sun, J. C. Y., & Yeh, K. P. C. (2017). The effects of attention monitoring with EEG biofeedback on university students' attention and self-efficacy: The case of anti-phishing instructional materials. Computers & Education, 106, 73-82. https://doi.org/10.1016/j.compedu.2016.12.003
  • Wang, C. Y. (2020). Differences in perception, understanding, and responsiveness of product design between experts and students: an early event-related potentials study. International Journal of Technology and Design Education, 23. https://doi.org/10.1007/s10798-020-09592-z
  • Wang, J. H., Antonenko, P., Keil, A., & Dawson, K. (2020). Converging subjective and psychophysiological measures of cognitive load to study the effects of instructorpresent video. Mind Brain and Education, 14(3), 279-291. https://doi.org/10.1111/mbe.12239
  • Wu, L., & Kim, M. (2019). See, touch, and feel: Enhancing young children's empathy learning through a tablet game. Mind Brain and Education, 13(4), 341-351. https://doi.org/10.1111/mbe.12218
  • Wu, S. F., Lu, Y. L., & Lien, C. J. (2021). Detecting students' flow states and their construct through electroencephalogram: Reflective flow experiences, balance of challenge and skill, and sense of control. Journal of Educational Computing Research, 58(8), 1515-1540, Article 0735633120944084. https://doi.org/10.1177/0735633120944084
  • Yang, X. Z., Cheng, P. Y., Lin, L., Huang, Y. M., & Ren, Y. Q. (2019). Can an integrated system of electroencephalography and virtual reality further the understanding of relationships between attention, meditation, flow state, and creativity?. Journal of Educational Computing Research, 57(4), 846-876. https://doi.org/10.1177/0735633118770800
  • Yang, X. Z., Lin, L., Cheng, P. Y., Yang, X., Ren, Y. Q., & Huang, Y. M. (2018). Examining creativity through a virtual reality support system. Etr&D-Educational Technology Research and Development, 66(5), 1231-1254. https://doi.org/10.1007/s11423-018-9604-z
  • Yang, X. Z., Lin, L., Wen, Y., Cheng, P. Y., Yang, X., & An, Y. (2020). Time-compressed audio on attention, meditation, cognitive load, and learning. Educational Technology & Society, 23(3), 16-26. https://www.jstor.org/stable/26926423
  • Youdell, D., Lindley, M., Shapiro, K., Sun, Y., & Leng, Y. (2020). From science wars to transdisciplinarity: the inescapability of the neuroscience, biology and sociology of learning. British Journal of Sociology of Education, 41(6), 881-899. https://doi.org/10.1080/01425692.2020.1784709
  • Zhai, X. S., Fang, Q. S., Dong, Y., Wei, Z. H., Yuan, J., Cacciolatti, L., & Yang, Y. L. (2018). The effects of biofeedback-based stimulated recall on self-regulated online learning: A gender and cognitive taxonomy perspective. Journal of Computer Assisted Learning, 34(6), 775-786. https://doi.org/10.1111/jcal.12284
  • Barraza, P., Dumas, G., Liu, H., Blanco-Gomez, G., van den Heuvel, M. I., Baart, M., & Pérez, A. (2019). Implementing EEG hyperscanning setups. MethodsX, 6, 428-436. https://doi.org/10.1016/j.mex.2019.02.021
  • Causa, M., La Paz, F., Radi, S., Oliver, J. P., Steinfeld, L., & Oreggioni, J. (2018, February). A 64-channel wireless EEG recording system for wearable applications. In 2018 IEEE 9th Latin American Symposium on Circuits & Systems (LASCAS), Puerto Vallarta, Mexico. https://ieeexplore.ieee.org/document/8399899
  • Chew, L. H., Teo, J., & Mountstephens, J. (2016). Aesthetic preference recognition of 3D shapes using EEG. Cognitive Neurodynamics, 10(2), 165-173. https://doi.org/10.1007/s11571-015-9363-z
  • Deuel, T. A., Pampin, J., Sundstrom, J., & Darvas, F. (2017). The Encephalophone: A novel musical biofeedback device using conscious control of electroencephalogram (EEG). Frontiers in Human Neuroscience, 11(213), 1-8. https://doi.org/10.3389/fnhum.2017.00213
  • Ekanayake, H. (2010). P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG? http://neurofeedback.visaduma.info/P300nEmotiv.pdf
  • Emotiv (2018). EPOC Flex. https://fccid.io/2ADIH-FLEX01/User-Manual/User-manual-4059138.pdf
  • Fiedler, P., Pedrosa, P., Griebel, S., Fonseca, C., Vaz, F., Supriyanto, E., F., Zanov, & Haueisen, J. (2015). Novel multipin electrode cap system for dry electroencephalography. Brain topography, 28(5), 647-656. https://doi.org/10.1007/s10548- 015-0435-5
  • Guomundsdoottir, K. (2011). Improving players' control over the NeuroSky brain-computer interface. [Doctoral dissertation]. https://skemman.is/bitstream/1946/9187/1/ImprovingPlayersControlOverNeuroSkyB CI_ResearchReport_kristingud08.pdf
  • Ma, M. Y., & Wei, C. C. (2016). A comparative study of children's concentration performance on picture books: age, gender, and media forms. Interactive Learning Environments, 24(8), 1922-1937. https://doi.org/10.1080/10494820.2015.1060505
  • Mahajan, R., Majmudar, C. A., Khatun, S., Morshed, B. I., & Bidelman, G. M. (2014, October). NeuroMonitor ambulatory EEG device: Comparative analysis and its application for cognitive load assessment. In 2014 IEEE Healthcare Innovation Conference (HIC) (pp. 133-136), Seattle, WA, USA. https://doi.org/10.1109/HIC.2014.7038892
  • Sanchez-Cifo, M. A., Montero, F., & López, M. T. (2021). MuseStudio: Brain activity data management library for low-cost EEG devices. Applied Sciences, 11(16), 7644. https://doi.org/10.3390/app11167644
  • Soufineyestani, M., Dowling, D., & Khan, A. (2020). Electroencephalography (EEG) technology applications and available devices. Applied Sciences, 10(21), 7453. https://doi.org/10.3390/app10217453
  • Vourvopoulos, A., & Liarokapis, F. (2012, June). Robot navigation using brain-computer interfaces. In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1785-1792), Liverpool, UK. https://doi.org/10.1109/TrustCom.2012.247
  • Yoghourdjian, V., Yang, Y., Dwyer, T., Lawrence, L., Wybrow, M., & Marriott, K. (2020). Scalability of network visualisation from a cognitive load perspective. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1677-1687. https://doi.org/10.48550/arXiv.2008.07944
APA Saygıner Ş, Balaman F, HANBAY TİRYAKİ S (2022). The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. , 184 - 201. 10.18009/jcer.1002588
Chicago Saygıner Şenol,Balaman Fatih,HANBAY TİRYAKİ Sevil The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. (2022): 184 - 201. 10.18009/jcer.1002588
MLA Saygıner Şenol,Balaman Fatih,HANBAY TİRYAKİ Sevil The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. , 2022, ss.184 - 201. 10.18009/jcer.1002588
AMA Saygıner Ş,Balaman F,HANBAY TİRYAKİ S The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. . 2022; 184 - 201. 10.18009/jcer.1002588
Vancouver Saygıner Ş,Balaman F,HANBAY TİRYAKİ S The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. . 2022; 184 - 201. 10.18009/jcer.1002588
IEEE Saygıner Ş,Balaman F,HANBAY TİRYAKİ S "The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study." , ss.184 - 201, 2022. 10.18009/jcer.1002588
ISNAD Saygıner, Şenol vd. "The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study". (2022), 184-201. https://doi.org/10.18009/jcer.1002588
APA Saygıner Ş, Balaman F, HANBAY TİRYAKİ S (2022). The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. Journal of Computer and Education Research, 10(19), 184 - 201. 10.18009/jcer.1002588
Chicago Saygıner Şenol,Balaman Fatih,HANBAY TİRYAKİ Sevil The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. Journal of Computer and Education Research 10, no.19 (2022): 184 - 201. 10.18009/jcer.1002588
MLA Saygıner Şenol,Balaman Fatih,HANBAY TİRYAKİ Sevil The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. Journal of Computer and Education Research, vol.10, no.19, 2022, ss.184 - 201. 10.18009/jcer.1002588
AMA Saygıner Ş,Balaman F,HANBAY TİRYAKİ S The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. Journal of Computer and Education Research. 2022; 10(19): 184 - 201. 10.18009/jcer.1002588
Vancouver Saygıner Ş,Balaman F,HANBAY TİRYAKİ S The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study. Journal of Computer and Education Research. 2022; 10(19): 184 - 201. 10.18009/jcer.1002588
IEEE Saygıner Ş,Balaman F,HANBAY TİRYAKİ S "The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study." Journal of Computer and Education Research, 10, ss.184 - 201, 2022. 10.18009/jcer.1002588
ISNAD Saygıner, Şenol vd. "The Current Trend in Educational Neuroscience Research: A Descriptive and Bibliometric Study". Journal of Computer and Education Research 10/19 (2022), 184-201. https://doi.org/10.18009/jcer.1002588