ENHANCING THE MOTIVATION AND LEARNING PERFORMANCE IN AN ONLINE CLASSROOM WITH THE USE OF NEUROMARKETING

Hedda Martina Šola, Fayyaz Hussain Qureshi, Sarwar Khawaja

Abstract


In recent years, the newly emerging discipline of neuromarketing, which employs brain (emotions and behaviour) research in an organisational context, has grown in prominence in academic and practice literature. With the increasing growth of online teaching, COVID-19 left no option for higher education institutions to go online. As a result, students who attend an online course are more prone to lose focus and attention, resulting in poor academic performance. Therefore, the primary purpose of this study is to observe the learner's behaviour while making use of an online learning platform. This study presents neuromarketing to enhance students' learning performance and motivation in an online classroom. Using a web camera, we used facial coding and eye-tracking techniques to study students' attention, motivation, and interest in an online classroom. In collaboration with Oxford Business College's marketing team, the Institute for Neuromarketing distributed video links via email, a student representative from Oxford Business College, the WhatsApp group, and a newsletter developed explicitly for that purpose to 297 students over the course of five days. To ensure the research was both realistic and feasible, the instructors in the videos were different, and students were randomly allocated to one video link lasting 90 seconds (n=142) and a second one lasting 10 minutes (n=155). An online platform for self-service called Tobii Sticky was used to measure facial coding and eye-tracking. During the 90-second online lecture, participants' gaze behaviour was tracked overtime to gather data on their attention distribution, and emotions were evaluated using facial coding. In contrast, the 10-minute film looked at emotional involvement. The findings show that students lose their listening focus when no supporting visual material or virtual board is used, even during a brief presentation. Furthermore, when they are exposed to a single shareable piece of content for longer than 5.24 minutes, their motivation and mood decline; however, when new shareable material or a class activity is introduced, their motivation and mood rise.

JEL: I20; I21

Article visualizations:

Hit counter


Keywords


neuromarketing, facial coding, eye-tracking, motivation, learning performance, online classroom

Full Text:

PDF

References


Adedoyin, O. B. & Soykan, E. 2020. Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments, Sep. 2020, 1-13. Retrieved from: https://doi.org/10.1080/10494820.2020.1813180

Afzal, H., Ali, I., Aslam Khan, M. & Hamid, K. 2010. A study of university students’ motivation and its relationship with their academic performance. Available at SSRN 2899435.

Alammary, A., Sheard, J., & Carbone, A. 2014. Blended learning in higher education: three different design approaches. Australas. J. Educ. Technol. 30, 440–454.

Al-Awni, A. 2016. Mood extraction using facial features to improve learning curves of students in e-learning systems. International journal of advanced computer science and applications, 7, 444-453.

Al-Khalifa, H. S. & George, R. P. 2010. Eye Tracking and e-Learning: Seeing Through Your Students' Eyes. eLearn, 2010.

Atchley, W., Akers, C. & Wingenbach, G. 2013. Comparison of Course Completion and Student Performance through Online and Traditional Courses. International Review of Research in Open and Distance Learning, 14 (4), 104-116.

Bailenson, J. N., Beall, A. C., Blascovich, J., Raimundo, M. & Weisbuch, M. Intelligent agents who wear your face: Users’ reactions to the virtual self. International Workshop on Intelligent Virtual Agents, 2001. Springer, 86-99.

Barrios, V. M. G., Gütl, C., Preis, A. M., Andrews, K., Pivec, M., Mödritscher, F. & Trummer, C. 2004. AdELE: A framework for adaptive e-learning through eye tracking. Proceedings of IKNOW, 609-616.

Bento, R. & Schuster, C. 2003. Participation: The Online Challenge. In A. Aggarwal (Ed.), Web-based education: Learning from experience (pp. 156-64). Hershey, PA: Idea Group Publishing

Bhatnagar, R., Kim, J. & Many, J. E. 2014. Candidate surveys on program evaluation: Examining Instrument reliability, validity and program effectiveness. American Journal of Educational Research, 2, 683-690.

Chae, S. W. & Lee, K. C. 2013. Exploring the effect of the human brand on consumers' decision quality in online shopping: An eye‐tracking approach. Online Information Review.

Colliot, T. & Jamet, E. 2018. Understanding the effects of a teacher video on learning from a multimedia document: an eye-tracking study. Educational Technology Research and Development, 66, 1415-1433.

Davies, J. & Graff, M. 2005. Performance in e-Performance in e-learning: Online participation and student grades. British Journal of Educational Technology, 36 (4), 657–663.

Dewan, M. A. A., Murshed, M. & Lin, F. 2019. Engagement detection in online learning: a review. Smart Learning Environments, 6, 1-20.

Dray, B. J., Lowenthal, P. R., Miszkiewicz, M. J., Ruiz‐Primo, M. A. & Marczynski, K. 2011. Developing an instrument to assess student readiness for online learning: A validation study. Distance Education, 32, 29-47.

Ekman, P. 1993. Facial expression and emotion. American psychologist, 48, 384.

Ekwunife-Orakwue, K. C. V. & Teng, T. L. 2014. The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Comput. Educ. 78, 414–427.

El Haddioui, I. & Khaldi, M. 2012. Learner behaviour analysis through eye tracking. International Journal of Computer Science Research and Application, 2, 11-18.

Ferguson, J. M., & De Felice, A. E. 2010. Length of online course and student satisfaction, perceived learning, and academic performance. Int. Rev. Res. Open Distance Learn. 11, 73–84

Galesic, M. & Yan, T. 2011. Use of eye tracking for studying survey response processes. Social and behavioural research and the internet: Advances in applied methods and research strategies, 349-370.

Garrison, D. R., & Kanuka, H. 2004. Blended learning: uncovering its transformative potential in higher education. Internet High. Educ. 7, 95–105.

Glazier, R. A. 2016 Building Rapport to Improve Retention and Success in Online Classes, Journal of Political Science Education

Heijstra, T. M. & Rafnsdottir, G. L. 2010. The Internet and academics' workload and work–family balance. The Internet and Higher Education, 13, 158-163.

Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H. & Van de Weijer, J. 2011. Eye tracking: A comprehensive guide to methods and measures, OUP Oxford.

Hyönä, J. 2010. The use of eye movements in the study of multimedia learning. Learning and Instruction, 20, 172-176.

Irie, K. 2003. What do we know about the language learning motivation of university students in Japan? Some patterns in survey studies. JALT Journal, 25, 86-100.

Johnson, L., Adams Becker, S., Estrada, V. & Freeman, A. 2015. NMC horizon report: 2015 higher education edition. Austin, Texas: The New Media Consortium. Retrieved from http://cdn.nmc.org/media/2015-nmc-horizon-report-HE-EN.pdf.

Jordan, K. 2015b. MOOC completion rates: The data. Retrieved from http://www.katyjordan.com/MOOCproject.html

Kerr, M. S., Rynearson, K., and Kerr, M. C. (2006). Student characteristics for online learning success. Internet Higher Educ. 9, 91–105.

Knight, B. A., Horsley, M. & Eliot, M. 2014. Eye tracking and the learning system: an overview. Current trends in eye tracking research, 281-285.

Law, K. M. Y., Geng, S. & Li, T. 2019. Student enrolment, motivation and learning performance in a blended learning environment: The medicating effects of social, teaching, and cognitive presence. Computers & Education, 136 (2009), 1-12.

Lee, N., Broderick, A. J. & Chamberlain, L. 2007. What is ‘neuromarketing’? A discussion and agenda for future research. International journal of psychophysiology, 63, 199-204.

Loh, K. K., Tan, B. Z. H. & Lim, S. W. H. 2016. Media multitasking predicts video-recorded lecture learning performance through mind wandering tendencies. Computers in Human Behaviour, 63, 943-947.

Loh, M.-P., Wong, Y.-P. & Wong, C.-O. Facial expression analysis in e-learning systems-the problems and feasibility. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), 2005. IEEE, 442-446.

Mason, L., Pluchino, P. & Tornatora, M. C. 2016. Using eye‐tracking technology as an indirect instruction tool to improve text and picture processing and learning. British Journal of Educational Technology, 47, 1083-1095.

Mattice, N. J. & Dixon, P. S. 1999. Student Preparedness for Distance Education.

Mayer, R. E. & Moreno, R. 2003. Nine ways to reduce cognitive load in multimedia learning. Educational psychologist, 38, 43-52.

Mundial, A. M. 2019. Declaración de Helsinki de la AMM-Principios éticos para las investigaciones médicas en seres humanos.

Olofsson, A. D. 2007. Participation in an educational online learning community. Journal of Educational Technology & Society, 10 (4), 28–38.

Paivio, A. 2013. Imagery and verbal processes, Psychology Press.

Pan, G., Zang, Y. & Wu, D. 2010. A survey on English learning motivation of students in Qingdao agricultural university. Journal of Language Teaching and Research, 1, 151-156.

Patterson, B., and Mcfadden C. 2009. Attrition in Online and Campus Degree Programs.” Online Journal of Distance Learning Administration 12(2).

Pintrich, P. R. 2004. A conceptual framework for assessing motivation and self-regulated learning in college students. Educational psychology review, 16, 385-407.

Pro, T. 2017. Eye tracking for research. URL: https://www.tobiipro.com.

Qureshi, F., Khawaja, S., Zia, T. 2020, Mature Undergraduate Students’ Satisfaction with Online Teaching During the Covid-19. European Journal of Education Studies, Vol 7, No 12

Risko, E. F., Anderson, N., Sarwal, A., Engelhardt, M. & Kingstone, A. 2012. Everyday attention: Variation in mind wandering and memory in a lecture. Applied Cognitive Psychology, 26, 234-242.

Robal, T., Zhao, Y., Lofi, C. & Hauff, C. Webcam-based attention tracking in online learning: A feasibility study. 23rd International Conference on Intelligent User Interfaces, 2018. 189-197.

Rothkrantz, L. Dropout rates of regular courses and MOOCs. International Conference on Computer Supported Education, 2016. Springer, 25-46.

Schacter, D. L. & Szpunar, K. K. 2015. Enhancing attention and memory during video-recorded lectures. Scholarship of Teaching and Learning in Psychology, 1, 60.

Sharma, P., Esengönül, M., Khanal, S. R., Khanal, T. T., Filipe, V. & Reis, M. J. Student concentration evaluation index in an e-learning context using facial emotion analysis. International Conference on Technology and Innovation in Learning, Teaching and Education, 2018. Springer, 529-538.

Sharples, M., Adams, A., Ferguson, R., Gaved, M., McAndrew, P., Rienties, B. & Whitelock, D. (2014). Innovating Pedagogy 2014: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. United Kingdom: The Open University. Retrieved from http://www.open.ac.uk/iet/main/files/iet-web/file/ecms/web-content/Innovating_Pedagogy_2014.pdf.

Shea, P. & Bidjerano, T. 2014. Does online learning impede degree completion? A national study of community college students. Comput. Educ. 75, 103–111.

Shen, L., Wang, M. & Shen, R. 2009. Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12, 176-189.

Skordis-Worrall, J., Haghparast-Bigdoli, H., Batura, N. & Hughes, J. 2015. Learning Online: A Case Study Exploring Student Perceptions and Experience of a Course in Economic Evaluation. International Journal of Teaching and Learning in Higher Education, 27 (3), 413-22. Retrieved from https://files.eric.ed.gov/fulltext/EJ1093737.pdf

Smith, P. J. 2005. Learning preferences and readiness for online learning. Educational psychology, 25, 3-12.

Stover, C. 2005. Measuring-and Understanding-Student Retention. Distance Education Rert 9(16): 1–7.

Sun, A., Li, Y.-J., Huang, Y.-M. & Li, Q 2017. Using facial expression to detect emotion in e-learning system: A deep learning method. International Symposium on Emerging Technologies for Education, Springer, 446-455.

Sungkur, R. K., Antoaroo, M. A. & Beeharry, A. 2016. Eye tracking system for enhanced learning experiences. Education and Information Technologies, 21, 1785-1806.

Terry, N. 2001. Assessing Enrollment and Attrition Rates for the Online MBA. The Journal 28(7): 64–68

Vonderwell, S., & Zachariach, S. 2005. Factors that influence participation in online learning. Journal of Research on Technology in Education, 38 (2), 213–230

Wilson, M. R., Vine, S. J., Bright, E., Masters, R. S., Defriend, D. & Mcgrath, J. S. 2011. Gaze training enhances laparoscopic technical skill acquisition and multi-tasking performance: a randomized, controlled study. Surgical endoscopy, 25, 3731-3739.

Yang, J. C., Quadir, B., Chen, N. S. & Miao, Q. 2016. Effects of online presence on learning performance in a blog-based online course. Internet High. Educ. 30, 11–20

Yuan, C. H. & Wu, Y. J. 2020. Mobile instant messaging or face-to-face? Group interactions in cooperative simulations. Comput. Hum. Behav. 113:106508

Zhan, Z., Zhang, L., Mei, H. & Fong, P. S. 2016. Online learners’ reading ability detection based on eye-tracking sensors. Sensors, 16, 1457.

Zurawicki, L. 2010. Neuromarketing: Exploring the brain of the consumer, Springer Science & Business Media.




DOI: http://dx.doi.org/10.46827/ejmms.v7i1.1169

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Hedda Martina Šola, Fayyaz Hussain Qureshi, Sarwar Khawaja

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The research works published in this journal are free to be accessed. They can be shared (copied and redistributed in any medium or format) and\or adapted (remixed, transformed, and built upon the material for any purpose, commercially and\or not commercially) under the following terms: attribution (appropriate credit must be given indicating original authors, research work name and publication name mentioning if changes were made) and without adding additional restrictions (without restricting others from doing anything the actual license permits). Authors retain the full copyright of their published research works and cannot revoke these freedoms as long as the license terms are followed.

Copyright © 2017-2023. European Journal of Management and Marketing Studies (ISSN 2501 - 9988) is a registered trademark of Open Access Publishing Group. All rights reserved.

This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library. All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All the research works published on this journal are meeting the Open Access Publishing requirements and standards formulated by Budapest Open Access Initiative (2002), the Bethesda Statement on Open Access Publishing (2003) and  Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (2003) and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License. Copyrights of the published research works are retained by authors.