Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow...

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Bibliographic Details
Published in:Mathematics (Basel) 2021-12, Vol.9 (24), p.3330
Main Authors: Masala, Mihai, Ruseti, Stefan, Rebedea, Traian, Dascalu, Mihai, Gutu-Robu, Gabriel, Trausan-Matu, Stefan
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
CAI
Cognitive style
Collaborative learning
Collaborative virtual environments
Computer assisted instruction
COVID-19
CSCL conversations
Datasets
Distance learning
educational technology
Kernels
Links
Machine learning
Mathematics
Natural Language Processing
Neural networks
Ontology
Pandemics
Semantics
string kernels
Strings
Target recognition
Task complexity
Verbal communication
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Summary:Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9243330