A new emotion–based affective model to detect student’s engagement
Detecting student's engagement is an important key to improve an e-learning system. An e-learning system adapted to learner emotions is considered as an innovative system. Among the challenges that face researcher is how to measure student's engagement depending on their emotions. During t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818309224 |
| _version_ | 1818595588380295168 |
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| author | Khawlah Altuwairqi Salma Kammoun Jarraya Arwa Allinjawi Mohamed Hammami |
| author_facet | Khawlah Altuwairqi Salma Kammoun Jarraya Arwa Allinjawi Mohamed Hammami |
| author_sort | Khawlah Altuwairqi |
| collection | DOAJ |
| description | Detecting student's engagement is an important key to improve an e-learning system. An e-learning system adapted to learner emotions is considered as an innovative system. Among the challenges that face researcher is how to measure student's engagement depending on their emotions. During the few years, several solutions were proposed to measure student’s engagement, but few solutions detect engagement level without consider if the student is learning or not. In this paper, we reviewed the current works of emotions and engagement level of student. According to that, we built our engagement level and linked them with the appropriate emotions. Then, we propose an affective model and a new process to detect final engagement level. The efficiency of the proposed Affective Model is shown experimentally by conducting a series of experiments. Firstly, we compute the Matching Score (MS) and Miss-matching Score (MisMS) for each engagement level. Secondly, we apply the new engagement level detection process on severe cases. Thirdly, we analyze all emotions in each level of engagement to detect strong emotions. We record matching score (MS) in range [71.2%, 100%]. Finally, we proposed some suggestions to improve the affective model. |
| first_indexed | 2024-12-16T11:18:24Z |
| format | Article |
| id | doaj.art-b31d094cd7b045778797d38687e1cad4 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-16T11:18:24Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-b31d094cd7b045778797d38687e1cad42022-12-21T22:33:31ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782021-01-0133199109A new emotion–based affective model to detect student’s engagementKhawlah Altuwairqi0Salma Kammoun Jarraya1Arwa Allinjawi2Mohamed Hammami3Department of Computer Science, King Abdul-Aziz University, Jeddah, Saudi Arabia; Corresponding author at: Department of Computer Science, King Abdul-Aziz University, 21589 Jeddah, Saudi Arabia.Department of Computer Science, King Abdul-Aziz University, Jeddah, Saudi Arabia; MIRACL-Laboratory, Sfax, TunisiaDepartment of Computer Science, King Abdul-Aziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, Faculty of Science, Sfax University, Sfax, Tunisia; MIRACL-Laboratory, Sfax, TunisiaDetecting student's engagement is an important key to improve an e-learning system. An e-learning system adapted to learner emotions is considered as an innovative system. Among the challenges that face researcher is how to measure student's engagement depending on their emotions. During the few years, several solutions were proposed to measure student’s engagement, but few solutions detect engagement level without consider if the student is learning or not. In this paper, we reviewed the current works of emotions and engagement level of student. According to that, we built our engagement level and linked them with the appropriate emotions. Then, we propose an affective model and a new process to detect final engagement level. The efficiency of the proposed Affective Model is shown experimentally by conducting a series of experiments. Firstly, we compute the Matching Score (MS) and Miss-matching Score (MisMS) for each engagement level. Secondly, we apply the new engagement level detection process on severe cases. Thirdly, we analyze all emotions in each level of engagement to detect strong emotions. We record matching score (MS) in range [71.2%, 100%]. Finally, we proposed some suggestions to improve the affective model.http://www.sciencedirect.com/science/article/pii/S1319157818309224Face expressionsEmotionsEngagement levelsAcademic emotionsAffective model |
| spellingShingle | Khawlah Altuwairqi Salma Kammoun Jarraya Arwa Allinjawi Mohamed Hammami A new emotion–based affective model to detect student’s engagement Journal of King Saud University: Computer and Information Sciences Face expressions Emotions Engagement levels Academic emotions Affective model |
| title | A new emotion–based affective model to detect student’s engagement |
| title_full | A new emotion–based affective model to detect student’s engagement |
| title_fullStr | A new emotion–based affective model to detect student’s engagement |
| title_full_unstemmed | A new emotion–based affective model to detect student’s engagement |
| title_short | A new emotion–based affective model to detect student’s engagement |
| title_sort | new emotion based affective model to detect student s engagement |
| topic | Face expressions Emotions Engagement levels Academic emotions Affective model |
| url | http://www.sciencedirect.com/science/article/pii/S1319157818309224 |
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