Integrating Sentiment Analysis with Learning Analytics for Improved Student
International Journal of Computational and Experimental Science and Engineering, ISSN: 2149-9144, Vol: 10, Issue: 4, Page: 1575-1583
2024
- 2Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The integration of sentiment analysis with learning analytics offers a novel approach to improving student outcomes by providing deeper insights into the emotional and cognitive states of learners. This research explores the use of sentiment analysis on student interactions, such as online discussions, assignments, and feedback, to assess the emotional tone of student engagement. By combining these sentiment insights with traditional learning analytics, which track academic progress and behavior patterns, this study aims to create a comprehensive model that enhances the identification of students at risk, tailor educational interventions, and fosters personalized learning experiences. The proposed approach not only improves the monitoring of student well-being and engagement but also supports the development of adaptive learning systems that respond to students’ emotional states. Results show that sentiment analysis integrated with learning analytics can provide real-time feedback for educators, enhancing student support and improving overall academic performance.
Bibliographic Details
International Journal of Computational and Experimental Science and Engineering
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