Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
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| Title: | Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations |
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| Language: | English |
| Authors: | Maria-Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu, Danielle S. McNamara, Stefan Trausan-Matu |
| Source: | Grantee Submission. 2022Paper presented at the International Conference on Smart Learning Ecosystems and Regional Development (6th, Bucharest, Romania, Jun 2021). |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2022 |
| Sponsoring Agency: | National Center for Education Research (NCER) (ED/IES) Office of Naval Research (ONR) (DOD) |
| Contract Number: | R305A180144 N000141912424 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Computational Linguistics, Longitudinal Studies, Technology Uses in Education, Teaching Methods, Learning Processes, Cooperative Learning, Computer Assisted Instruction, Dialogs (Language), Semantics, Computer Mediated Communication, Models, Algorithms, Intelligent Tutoring Systems, COVID-19, Pandemics, Online Courses, Undergraduate Students, Computer Science Education, Visual Aids |
| DOI: | 10.1007/978-981-16-3930-2_6 |
| 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. [This paper was published in: "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," edited by Ó. Mealha et al., Springer Nature Singapore Pte Ltd., 2022, pp. 67-78.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2023 |
| Accession Number: | ED637572 |
| Database: | ERIC |
| 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. [This paper was published in: "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," edited by Ó. Mealha et al., Springer Nature Singapore Pte Ltd., 2022, pp. 67-78.] |
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| DOI: | 10.1007/978-981-16-3930-2_6 |
