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

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Title: Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning
Language: English
Authors: Bóbó, Míria L. D. R., Campos, Fernanda, Stroele, Victor (ORCID 0000-0001-6296-8605), David, José Maria N., Braga, Regina (ORCID 0000-0002-4888-0778), Torrent, Tiago Timponi
Source: International Journal of Distance Education Technologies. 2022 20(1).
Availability: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Peer Reviewed: Y
Page Count: 24
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Dropout Prevention, Psychological Patterns, Learner Engagement, Electronic Learning, At Risk Students, Student Attitudes, Student Motivation, Foreign Countries, College Students, Virtual Classrooms, Natural Language Processing, Phrase Structure
Geographic Terms: Brazil
DOI: 10.4018/IJDET.305237
ISSN: 1539-3100
1539-3119
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 author 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 student school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of student risks of dropout.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1343950
Database: ERIC
FullText Text:
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  Data: Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning
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  Data: <searchLink fieldCode="AR" term="%22Bóbó%2C+Míria+L%2E+D%2E+R%2E%22">Bóbó, Míria L. D. R.</searchLink><br /><searchLink fieldCode="AR" term="%22Campos%2C+Fernanda%22">Campos, Fernanda</searchLink><br /><searchLink fieldCode="AR" term="%22Stroele%2C+Victor%22">Stroele, Victor</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6296-8605">0000-0001-6296-8605</externalLink>)<br /><searchLink fieldCode="AR" term="%22David%2C+José+Maria+N%2E%22">David, José Maria N.</searchLink><br /><searchLink fieldCode="AR" term="%22Braga%2C+Regina%22">Braga, Regina</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4888-0778">0000-0002-4888-0778</externalLink>)<br /><searchLink fieldCode="AR" term="%22Torrent%2C+Tiago+Timponi%22">Torrent, Tiago Timponi</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Distance+Education+Technologies%22"><i>International Journal of Distance Education Technologies</i></searchLink>. 2022 20(1).
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  Data: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
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  Data: 24
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  Data: <searchLink fieldCode="DE" term="%22Dropout+Prevention%22">Dropout Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Patterns%22">Psychological Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+Classrooms%22">Virtual Classrooms</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Phrase+Structure%22">Phrase Structure</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Brazil%22">Brazil</searchLink>
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  Data: 10.4018/IJDET.305237
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  Data: 1539-3100<br />1539-3119
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  Label: Abstract
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  Data: 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 author 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 student school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of student risks of dropout.
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  Data: 2022
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  Data: EJ1343950
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        PageCount: 24
    Subjects:
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Psychological Patterns
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      – SubjectFull: Learner Engagement
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      – SubjectFull: College Students
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      – SubjectFull: Virtual Classrooms
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      – TitleFull: Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning
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