Abstract
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.
Highlights
With an increased prevalence of online presence, accelerated by the current COVID-19 pandemic [1], online messaging applications are gaining an increased popularity
Our supervised neural network is compared with state-of-the-art methods for answer selection and semantic links detection
Computer-Supported Collaborative Learning environments have shown an increased usage, especially when it comes to problem-solving tasks, being very useful in the context of online activities imposed by the COVID-19 pandemic
Summary
With an increased prevalence of online presence, accelerated by the current COVID-19 pandemic [1], online messaging applications are gaining an increased popularity. Online social networks make a significant percentage of these platforms, but standalone chat applications are widely adopted. These platforms are not used only for entertainment purposes, but their applications cover various activities, including and even promoting collaborative learning and creative thinking [2]. Artificial Intelligence techniques have been widely employed in various educational settings [3,4], ranging from classifying learning styles [5,6], to finding active collaborators within a group [7], to providing personalized feedback [8,9], and even customizing curriculum content [10]. In collaborative learning settings, automated systems classify students based on their implication and collaboration activity, and provide information to support and enhance students’
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