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Exploring the Triangulation of Dimensionality Reduction When Interpreting Multimodal Learning Data from Authentic Settings

  • Pankaj ChejaraEmail author
  • Luis P. Prieto
  • Adolfo Ruiz-Calleja
  • María Jesús Rodríguez-Triana
  • Shashi Kant Shankar
Conference paper
  • 783 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11722)

Abstract

Multimodal Learning Analytics (MMLA) has sparked researcher interest in investigating learning in real-world settings by capturing learning traces from multiple sources of data. Though multimodal data offers a more holistic picture of learning, its inherent complexity makes it difficult to understand and interpret. This paper illustrates the use of dimensionality reduction (DR) to find a simple representation of multimodal learning data collected from co-located collaboration in authentic settings. We employed multiple DR methods and used triangulation to interpret their result which in turn provided a more simplistic representation. Additionally, we also show how unexpected events in authentic settings (e.g., missing data) can affect the analysis results.

Keywords

Co-located collaboration Multimodal Learning Analytics Dimensionality reduction method Computer-supported collaborative learning 

Notes

Acknowledgements

This research has been partially funded by the European Union via the European Regional Development Fund, in the context of CEITER and Next-Lab (Horizon 2020 Research and Innovation Programme, grant no. 669074 and 731685).

References

  1. 1.
    Pardo, A., Delgado Kloos, C.: Stepping out of the box: towards analytics outside the learning management system. In: 1st International Conference on Learning Analytics and Knowledge (LAK 2011), pp. 163–167. ACM, New York (2011)Google Scholar
  2. 2.
    Ochoa, X.: Multimodal Learning Analytics. In: Lang, C., Siemens, G., Wise, A.F., Gaevic, D. (eds.) The Handbook of Learning Analytics, Alberta, Canada, pp. 129–141. Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.011CrossRefGoogle Scholar
  3. 3.
    Chua, Y.H.V., Dauwels, J., Tan, S.C.: Technologies for automated analysis of co-located, real-life, physical learning spaces. In: Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK 2019), pp. 11–20. ACM, New York (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pankaj Chejara
    • 1
    Email author
  • Luis P. Prieto
    • 1
  • Adolfo Ruiz-Calleja
    • 2
  • María Jesús Rodríguez-Triana
    • 1
  • Shashi Kant Shankar
    • 1
  1. 1.Tallinn UniversityTallinnEstonia
  2. 2.GSIC-EMIC GroupUniversity of ValladolidValladolidSpain

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