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

Conference paper
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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tallinn UniversityTallinnEstonia
  2. 2.GSIC-EMIC GroupUniversity of ValladolidValladolidSpain

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