Abstract
Chat conversations between more than two participants are often used in Computer Supported Collaborative Learning (CSCL) scenarios because they enhance collaborative knowledge sharing and sustain creativity. However, multi-participant chats are more difficult to follow and analyze due to the complex ways in which different discussion threads and topics can interact. This paper introduces a novel method based on neural networks for detecting implicit links that uses features computed with string kernels and word embeddings. In contrast to previous experiments with an accuracy of 33%, we obtained a considerable increase to 44% for the same dataset. Our method represents an alternative to more complex deep neural networks that cannot be properly used due to overfitting on limited training data.
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Acknowledgements
This research was partially supported by the following projects: FP7-212578 LTfLL, EC H2020-644187 Realising an Applied Gaming Eco-system (RAGE), and POC-2015 P39-287 IAVPLN.
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Masala, M., Ruseti, S., Gutu-Robu, G., Rebedea, T., Dascalu, M., Trausan-Matu, S. (2018). Identifying Implicit Links in CSCL Chats Using String Kernels and Neural Networks. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_37
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