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Let’s Listen to the Data: Sonification for Learning Analytics

  • Eric SanchezEmail author
  • Théophile SanchezEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1112)

Abstract

This paper falls in the field of playing analytics. It deals with an empirical work dedicated to explore the potential of data sonification (i.e. the conversion of data into sound that reflects their objective properties or relations). Data sonification is proposed as an alternative to data visualization. We applied data sonification for the analysis of gameplays and players’ strategies during a session dedicated to game-based learning. The data of our study (digital traces) was collected from 200 pre-service teachers who played Tamagocours, an online collaborative multiplayer game dedicated to learn the rules (i.e. copyright) that comply with the policies for the use of digital resources in an educational context. For one typical individual (parangon) for each of the 5 categories of players, the collected digital traces were converted into an audio format so that the actions that they performed become listenable. A specific software, SOnification of DAta for Learning Analytics (SODA4LA), was developed for this purpose. The first results show that different features of the data can be recognized from data listening. These results also enable for the identification of different parameters that should be taken into account for the sonification of diachronic data. We consider that this study open new perspectives for playing analytics. Thus we advocate for new research aiming at exploring the potential of data sonification for the analysis of complex and diachronic datasets in the field of educational sciences.

Keywords

Sonification Playing analytics Data visualisation Game-based learning Learning Analytics 

References

  1. 1.
    Avanzo, S., Barbera, R., De Mattia, F., La Rocca, G., Sorrentino, M., Vicinanza, D.: Data sonification of volcano seismograms and sound/timbre reconstruction of ancient musical instruments with grid infrastructures. Procedia Comput. Sci. 1(1), 397–406 (2010)CrossRefGoogle Scholar
  2. 2.
    Berthold, M.R., et al.: KNIME-the konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explor. Newsl. 11(1), 26–31 (2009)CrossRefGoogle Scholar
  3. 3.
    Bregman, A.S.: Auditory Scene Analysis: The Perceptual Organization of Sound. The MIT Press, Cambridge (1990)CrossRefGoogle Scholar
  4. 4.
    Casado, R., Guin, N., Champin, P.-A., Lefevre, M.: kTBS4LA: une plateforme d’analyse de traces fondée sur une modélisation sémantique des traces. In: Méthodologies et outils pour le recueil, l’analyse et la visualisation des traces d’interaction - ORPHEE-RDV, Font-Romeu, France, January 2017 (2017)Google Scholar
  5. 5.
    Design-Based Research Collective: Design-based research: an emerging paradigm for educational inquiry. Educ. Res. 32(1), 5–8 (2003)CrossRefGoogle Scholar
  6. 6.
    Demšar, J., et al.: Orange: data mining toolbox in python. J. Mach. Learn. Res. 14(1), 2349–2353 (2013)zbMATHGoogle Scholar
  7. 7.
    Handel, S.: Listening: An Introduction to the Perception of Auditory Events. The MIT Press, Cambridge (1993)Google Scholar
  8. 8.
    Hermann, T., Hunt, A., Neuhoff, J.G.: The Sonification Handbook. Logos Verlag, Berlin (2011)Google Scholar
  9. 9.
    Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench, pp. 357–361 (1994).  https://doi.org/10.1109/ANZIIS.1994.396988
  10. 10.
    Kramer, G., et al.: The sonification report: status of the field and research agenda. Report prepared for the national science foundation by members of the international community for auditory display. International Community for Auditory Display (ICAD), Santa Fe, NM (1999)Google Scholar
  11. 11.
    Sanchez, E., Mandran, N.: Exploring competition and collaboration behaviors in game-based learning with playing analytics. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 467–472. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66610-5_44CrossRefGoogle Scholar
  12. 12.
    Sanchez, E., Martinez-Emin, V., Mandran, N.: Jeu-game, jeu-play, vers une modélisation du jeu. Une étude empirique à partir des traces numériques d’interaction du jeu Tamagocours. Sciences et Technologies de l’Information et de la Communication pour l’Éducation et la Formation 22(1), 9–44 (2015)CrossRefGoogle Scholar
  13. 13.
    Sanchez, E., Monod-Ansaldi, R., Vincent, C., Safadi-Katouzian, S.: A praxeological perspective for the design and implementation of a digital role-play game. Educ. Inf. Technol. 22(6), 2805–2824 (2017)CrossRefGoogle Scholar
  14. 14.
    Speeth, S.D.: Seismometer sounds. J. Acoust. Soc. Am. 33(7), 909–916 (1961)CrossRefGoogle Scholar
  15. 15.
    RStudio Team, et al.: Rstudio: Integrated Development for R. RStudio, Inc., Boston, 42:14 (2015). http://www.rstudio.com
  16. 16.
    Worrall, D.: Using sound to identify correlations in market data. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K. (eds.) CMMR/ICAD-2009. LNCS, vol. 5954, pp. 202–218. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12439-6_11CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of FribourgFribourgSwitzerland
  2. 2.Paris-Sud UniversityOrsayFrance

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