An Approach for Thematic Relevance Analysis Applied to Textual Contributions in Discussion Forums

An Approach for Thematic Relevance Analysis Applied to Textual Contributions in Discussion Forums

Crystiano José Richard Machado (University of Pernambuco, Recife, Brazil), Alexandre Magno Andrade Maciel (University of Pernambuco, Recife, Brazil), Rodrigo Lins Rodrigues (University Federal Rural of Pernambuco, Recife, Brazil) and Ronaldo Menezes (University of Exeter, Exeter, UK)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJDET.2019070103
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Discussion forums in learning management systems (LMS) have been shown to promote student interaction and contribute to the collaborative practice in the teaching-learning process. By evaluating the postings, teachers can identify students with learning difficulties. However, due to the large volume of posts that are generated on a daily basis in these environments, manual analysis becomes impractical. This article proposes a mechanism to support teaching through the thematic relevance analysis of the posts made by students in discussion forums. For this, text mining and metrics from network science were used to process and extract characteristics of the texts. Then, the processed texts were classified through supervised learning algorithms. The results show that the use of these techniques may generate potentially useful indicators for teachers to help them improve their pedagogical practices.
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Several scientific studies have been carried out in DL environments with a focus on the investigation of aspects associated with both the communication and the interaction of students in LMS (Chen & Looi, 2017; Machado, Lima, Maciel, & Rodrigues, 2016). The activities related to the communication and interaction of students in these virtual environments promote the Computer-Supported Collaborative Learning (CSCL) which, according to Bogarín, Cerezo, and Romero (2018), “is characterized by the sharing and construction of knowledge between participants using technology as their primary means of communication or as a common resource” (p. 10). From the computational point of view, several techniques have achieved promising results, among them Text Mining and Network Science deserve our special attention as they are related to the scope of this work.

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