TY - GEN
T1 - Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
AU - Dascalu, Maria Dorinela
AU - Ruseti, Stefan
AU - Dascalu, Mihai
AU - McNamara, Danielle S.
AU - Trausan-Matu, Stefan
N1 - Funding Information:
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.
Funding Information:
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.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Computer-supported collaborative learning
KW - Dialogism
KW - Language models
KW - Semantic chains
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U2 - 10.1007/978-981-16-3930-2_6
DO - 10.1007/978-981-16-3930-2_6
M3 - Conference contribution
AN - SCOPUS:85115255751
SN - 9789811639296
T3 - Smart Innovation, Systems and Technologies
SP - 67
EP - 78
BT - Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education - Proceedings of the 6th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2021
A2 - Mealha, Óscar
A2 - Dascalu, Mihai
A2 - Di Mascio, Tania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2021
Y2 - 24 June 2021 through 25 June 2021
ER -