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Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education pp 67–78Cite as

Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 249))

Abstract

The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and stimulating collaboration between participants has become more stringent, as human monitoring of the increasing volume of conversations becomes overwhelming. This paper introduces a method grounded in dialogism for evaluating students’ involvement in chat conversations based on semantic chains computed using language models. These semantic chains reflect emergent voices from dialogism that span and interact throughout the conversation. Our integrated method uses contextual information captured by BERT transformer models to identify links in a chain that connects semantically related concepts from a voice uttered by one or more participants. Two types of visualizations were generated to depict the longitudinal propagation and the transversal inter-animation of voices within the conversation. In addition, a list of handcrafted features derived from the constructed chains and computed for each participant is introduced. Several machine learning algorithms were tested using these features to evaluate the extent to which semantic chains are predictive of student involvement in chat conversations.

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Acknowledgements

The work was funded by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, project number TE 70 PN-III-P1-1.1-TE-2019-2209, ATES—“Automated Text Evaluation and Simplification.” This research was also supported in part by the Institute of Education Sciences (R305A180144) and the Office of Naval Research (N00014-19-1-2424). The opinions expressed are those of the authors and do not represent views of the IES or ONR.

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Authors and Affiliations

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

    Maria-Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu & Stefan Trausan-Matu

  2. Academy of Romanian Scientists, Str. Ilfov, Nr. 3, 050044, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

  3. Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, USA

    Danielle S. McNamara

Authors
  1. Maria-Dorinela Dascalu
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  2. Stefan Ruseti
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  3. Mihai Dascalu
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  4. Danielle S. McNamara
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  5. Stefan Trausan-Matu
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Corresponding author

Correspondence to Mihai Dascalu .

Editor information

Editors and Affiliations

  1. Department of Communication and Art/DigiMedia, University of Aveiro, Aveiro, Portugal

    Óscar Mealha

  2. Department of Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Mihai Dascalu

  3. Department of Engineering Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy

    Tania Di Mascio

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Dascalu, MD., Ruseti, S., Dascalu, M., McNamara, D.S., Trausan-Matu, S. (2022). Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations. In: Mealha, Ó., Dascalu, M., Di Mascio, T. (eds) Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems and Technologies, vol 249. Springer, Singapore. https://doi.org/10.1007/978-981-16-3930-2_6

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