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Individual Differences in Music Video Interaction: An exploratory Analysis

Published:09 July 2017Publication History

ABSTRACT

Learning through video watching has been popular through the education community and is considered as a common choice especially for self-directed informal learning. However, the learner in this situation acts only as a passive consumer and does not receives any feedback for improving his/her performance, an element important in any educational context. Studies in music psychology reveal that gender, perceptual, and cognitive, differences, along with the level of music education of the individual, should be considered when support is generated to a person who is watching a music video for educational purposes. In this sense, individual differences should be exploited when designing an adaptive learning support aiming to suit the individual in music learning. In this line, this paper presents an exploratory study into interaction data of music experts and amateurs when they were actively watching a music video. Linguistic analysis is also employed for taking an insight into the written comments provided by participants at several timepoints in the music videos. Results reveal significant differences between genders in their interaction behavior but also in their perception processing of the music videos, reflected in their comments. Suggestions are provided based on the results on how these can be utilized for the design of personalized support in informal music education.

References

  1. Vieira, I., Lopes, A. P., Soares, F. (2014). The potential benefits of using videos in higher education. Proc. EDULEARN14 Conf. (pp. 0750-0756). IATED Publications.Google ScholarGoogle Scholar
  2. Kravčík, M., Klamma, R. (2011). On psychological aspects of learning environments design. In European Conference on Technology Enhanced Learning (pp. 436--441). Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Kruse, N. B., and Veblen, K. K. (2012) Music teaching and learning online: Considering YouTube instructional videos. Journal of Music, Technology & Education 5(1), 77--87.Google ScholarGoogle ScholarCross RefCross Ref
  4. Karppinen, P. (2005). Meaningful learning with digital and online videos: Theoretical perspectives. AACE Journal 13(3), 233--250.Google ScholarGoogle Scholar
  5. Hyde, J. S. and McKinley, N. M. (1997) Gender differences in cognition. Gender differences in human cognition, 30--51.Google ScholarGoogle Scholar
  6. Kölsch, S., Maess, B., Grossmann, T. Friederici, A. D. (2003) Electric brain responses reveal gender differences in music processing. NeuroReport 14(5),709--713.Google ScholarGoogle ScholarCross RefCross Ref
  7. Caplan, P. J., Crawford, M., Hyde, J. S. Richardson TE. J. (1997). Gender Differences in Human Cognition. Counterpoints: Cognition, Memory, and Language Series. Oxford University Press.Google ScholarGoogle Scholar
  8. Folkestad, G. (2006) Formal and informal learning situations or practices vs formal and informal ways of learning. British journal of music education 23 (2), 135--145.Google ScholarGoogle Scholar
  9. Waldron, J. (2013) User-generated content, YouTube and participatory culture on the Web: Music learning and teaching in two contrasting online communities. Music Education Research 15(3), 257--274.Google ScholarGoogle ScholarCross RefCross Ref
  10. Brook, J., Upitis, R. (2015) Can an online tool support contemporary independent music teaching and learning? Music Education Research 17(1), 34--47.Google ScholarGoogle ScholarCross RefCross Ref
  11. Yousef, A. M. F., Chatti, M. A., Schroeder, U. (2014). The state of video-based learning: A review and future perspectives. Int. J. Adv. Life Sci, 6(3/4), 122--135Google ScholarGoogle Scholar
  12. Mitrovic, A., Dimitrova, V., Weerasinghe, A., Lau, L. (2016) Reflective Experiential Learning: Using Active Video Watching for Soft Skills Training. In Proceedings of the 24th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education.Google ScholarGoogle Scholar
  13. Dimitrova, V. Mitrovic, A., Piotrkowicz, A., Lau, L. Weerasinghe, A. (2017). Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching. In Proceedings of the 2017 Conference on User Modeling Adaptation and Personalization (UMAP '17). ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hallam, S., Cross, I., Thaut, M. (2011). Oxford handbook of music psychology. Oxford University PressGoogle ScholarGoogle Scholar
  15. Platz, F., and Kopiez, R. (2013) When the first impression counts: Music performers, audience and the evaluation of stage entrance behaviour. Musicae Scientiae 17(2), 167--197.Google ScholarGoogle ScholarCross RefCross Ref
  16. Thompson, W. F., and Russo, F. A. (2007) Facing the music. Psychological Science 18( 9), 756--757.Google ScholarGoogle ScholarCross RefCross Ref
  17. Sams, M., Aulanko, R., Hämäläinen, M., Hari, R., Lounasmaa, O. V., Lu, S.T., Simola, J. (1991) Seeing speech: visual information from lip movements modifies activity in the human auditory cortex. Neuroscience letters 127(1), 141--145.Google ScholarGoogle Scholar
  18. Bergeron, V., McIver Lopes, D. (2009) Hearing and seeing musical expression. Philosophy and Phenomenological Research 78(1), 1--16.Google ScholarGoogle ScholarCross RefCross Ref
  19. Harrar, V., Harris, L. R. (2008) The effect of exposure to asynchronous audio, visual, and tactile stimulus combinations on the perception of simultaneity. Experimental brain research 186(4), 517--524.Google ScholarGoogle Scholar
  20. Tausczik, Y. R., Pennebaker J. W. (2010) The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology 29(1), 24--54. Harvard.Google ScholarGoogle ScholarCross RefCross Ref
  21. Litvak, M., Otterbacher, J., Ang, C. S. Atkins, D. (2016) Social and linguistic behavior and its correlation to trait empathy. PEOPLES 2016, 128.Google ScholarGoogle Scholar
  22. Tsay, C.-J. (2013) Sight over sound in the judgment of music performance. Proceedings of the National Academy of Sciences 110(36), 14580--14585.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
    July 2017
    456 pages
    ISBN:9781450350679
    DOI:10.1145/3099023

    Copyright © 2017 ACM

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    Publication History

    • Published: 9 July 2017

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