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 |
| 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 |
| 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. |
|---|---|
| ISSN: | 1539-3100 1539-3119 |
| DOI: | 10.4018/IJDET.305237 |
