Sentiment Analysis in an Affective Intelligent Tutoring System
Abstract
This paper presents an implementation of Sentiment Analysis module that works in an Affective Intelligent Tutoring System. Later, we show the results of evaluating this module and some experiments we made with students. We consider that the main contribution of this work is that with this module, the system will be able to analyze the student's feedback using sentiment analysis techniques and provide useful information to the course administrators teachers for improving the teaching material.
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There is a direct correlation between emotions and learning.
“Considering a direct correlation between emotions and learning [40] recently, ITS have also started focusing on emotional state of students while learning to offer a more contextualized learning experience [24].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [40]
Cites
Sentiment analysis in Facebook and its application to e-learning
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Effectiveness of feedback: the students’ perspective
Poulos, A., & Mahony, M. J. (2008) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930601127869 · Cited by 544 · see citing works
Sentiment classification: The contribution of ensemble learning
Wang, G., Sun, J., Ma, J., Xu, K. S. J., & Gu, J. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.08.002 · Cited by 378 · see citing works
Liu, B. (2011) · Springer eBooks · doi:10.1007/978-3-642-19460-3 · Cited by 349 · see citing works
Mining sentiments in SMS texts for teaching evaluation
Leong, C. K., Lee, Y. H., & Mak, W. K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.08.113 · Cited by 116 · see citing works
Feelings About Feedback: The Role of Emotions in Assessment for Learning
Rowe, A. D. (2016) · The enabling power of assessment · doi:10.1007/978-981-10-3045-1_11 · Cited by 103 · see citing works
SA-E: Sentiment Analysis for Education
Altrabsheh, N., Gaber, M. M., & Cocea, M. (2013) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-264-6-353 · Cited by 101 · see citing works
Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11298-5_5 · Cited by 98 · see citing works
Exploiting sentiment analysis to track emotions in students' learning diaries
Munezero, M., Montero, C. S., Mozgovoy, M., & Sutinen, E. (2013) · Koli Calling International Conference on Computing Education Research · doi:10.1145/2526968.2526984 · Cited by 91 · see citing works
An Affective Learning Environment for Java
Cabada, R. Z., Estrada, M. L. B., González-Hernández, F., & Bustillos, R. O. (2015) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2015.53 · Cited by 31 · see citing works
Cummins, S., Burd, L., & Hatch, A. O. (2010) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2010.186 · Cited by 15 · see citing works
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Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425 · see citing works
Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1332 · Cited by 193 · see citing works
- Authors
- Barron-Estrada, M. L., Zatarain-Cabada, R., Oramas-Bustillos, R., & Gonzalez-Hernandez, F.
- Venue
- 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 394-397
- OpenAlex
- W2742658477
- Cited by
- 32
- References
- 11
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 7 · 2020: 2 · 2019: 6 · 2018: 3 · 2017: 1
- FWCI
- 1.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Advanced Learning Technologies (ICALT) (publishedVersion)
- Funders
- Tecnológico Nacional de México
- Consejo Nacional de Ciencia y Tecnología
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- sentiment analysis, student feedback, student emotions, course management
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