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Help Me Understand This Conversation: Methods of Identifying Implicit Links Between CSCL Contributions

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Lifelong Technology-Enhanced Learning (EC-TEL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11082))

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Abstract

Multi-participant chat conversations are one of the most frequently employed Computer Supported Collaborative Learning tools due to their ease of use. Moreover, chats enhance knowledge sharing, sustain creativity and aid in collaborative problem solving. Nevertheless, the manual analysis of multi-participant chats is a difficult task due to the mixture of different topics and the inter-twinning of multiple discussion threads during the same conversation. Several tools that employ Natural Language Processing techniques have been developed to automatically identify links between contributions in order to facilitate the tracking of topics and of discussion threads, as well as to highlight key contributions in terms of follow-up impact. This paper proposes a novel method for detecting implicit links based on features computed using string kernels and word embeddings, combined with neural networks. This method significantly outperforms previous results on the same dataset. Due to its smaller size, our model represents an alternative to more complex deep neural networks, especially when limited training data is available as is the case of CSCL chats in a specific domain.

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Notes

  1. 1.

    https://catalog.ldc.upenn.edu/LDC2011T07.

  2. 2.

    http://string-kernels.herokuapp.com/.

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Acknowledgements

This research was partially supported by the FP7 2008-212578 LTfLL, EC H2020-644187 Realising an Applied Gaming Eco-system (RAGE), and POC-2015 P39-287 IAVPLN projects.

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Authors and Affiliations

  1. University Politehnica of Bucharest, 313 Splaiul Independentei, 060042, Bucharest, Romania

    Mihai Masala, Stefan Ruseti, Gabriel Gutu-Robu, Traian Rebedea, Mihai Dascalu & Stefan Trausan-Matu

  2. Academy of Romanian Scientists, 54 Splaiul Independentei, 050094, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

  3. Autonomous Systems, 22 Tudor Vladimirescu, 050883, Bucharest, Romania

    Mihai Masala, Stefan Ruseti & Traian Rebedea

Authors
  1. Mihai Masala
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  2. Stefan Ruseti
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  3. Gabriel Gutu-Robu
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  4. Traian Rebedea
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  5. Mihai Dascalu
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  6. Stefan Trausan-Matu
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Corresponding author

Correspondence to Mihai Masala .

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Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Viktoria Pammer-Schindler

  2. Pontificia Universidad Católica de Chile, Providencia, Santiago de Chile, Chile

    Mar Pérez-Sanagustín

  3. DIPF | Leibniz Institute for Research and Information in Education, Frankfurt, Germany

    Hendrik Drachsler

  4. RayCom BV, Utrecht, Utrecht, The Netherlands

    Raymond Elferink

  5. Open University Netherlands, Heerlen, The Netherlands

    Maren Scheffel

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Masala, M., Ruseti, S., Gutu-Robu, G., Rebedea, T., Dascalu, M., Trausan-Matu, S. (2018). Help Me Understand This Conversation: Methods of Identifying Implicit Links Between CSCL Contributions. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_37

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  • DOI: https://doi.org/10.1007/978-3-319-98572-5_37

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