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Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning

Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning

Míria L. D. R. Bóbó, Fernanda Campos, Victor Stroele, José Maria N. David, Regina Braga, Tiago Timponi Torrent
Copyright: © 2022 |Volume: 20 |Issue: 1 |Pages: 24
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781799893424|DOI: 10.4018/IJDET.305237
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MLA

Bóbó, Míria L. D. R., et al. "Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning." IJDET vol.20, no.1 2022: pp.1-24. http://doi.org/10.4018/IJDET.305237

APA

Bóbó, M. L., Campos, F., Stroele, V., David, J. M., Braga, R., & Torrent, T. T. (2022). Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning. International Journal of Distance Education Technologies (IJDET), 20(1), 1-24. http://doi.org/10.4018/IJDET.305237

Chicago

Bóbó, Míria L. D. R., et al. "Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning," International Journal of Distance Education Technologies (IJDET) 20, no.1: 1-24. http://doi.org/10.4018/IJDET.305237

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Abstract

Dropping out of school comes from a long-term disengagement process with social and economic consequences. Being able to predict students' behavior earlier can minimize their failures and disengagement. This article presents the SASys architecture, based on a lexical approach and a polarized frame network. Its main goal is to define the author's sentiment in texts and increase the assertiveness of detecting the sentence's emotional state by adding authors' information and preferences. The author's emotional state begins with the phrase extraction from Virtual Learning Environments; then, pre-processing techniques are applied in the text, which is submitted to the complex frame network to identify words with polarity and the author's text sentiment. The flow ends with the identification of the author's emotional state. The proposal was evaluated by a case study, applying the Sentiment Analysis approach to the students' school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of students' risks of dropout.