Anwar, Aamir, Rehman, Ikram ORCID: https://orcid.org/0000-0003-0115-9024, Masralla, M.M., Khattak, S.B.A. and Khilji, Nasrullah (2023) Sentiment Analysis and Student Emotions: Improving Satisfaction in Online Learning Platforms. In: 2023 IEEE International Smart Cities Conference (ISC2), 24-27 Sep 2023, Bucharest, Romania.

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Abstract

Human emotion recognition using artificial intelligence is among the most prominent research areas. Human-Computer Interaction (HCI) and Sentiment Analysis (SA) are extensively used to detect human emotions. The role of students' sentiments and emotions becomes vital while using online learning platforms due to the need for physical interaction between instructor and learner to understand each other compared to face-to-face learning. This study identifies challenges and issues of online learning and student satisfaction while using online learning platforms. In addition, we analysed the importance of sentiment analysis for students' satisfaction while using online learning platforms. This study retrieved 112 research articles/reports using keywords such as ‘Online Learning’, ‘Online Learning Post-COVID-19’, ‘Student Satisfaction’, ‘Student Sentiment Analysis’, ‘Learning Environments’ and ‘Student Performance in Online Learning’. Among 112 research articles, 37 were identified after excluding duplicate and irrelevant research articles based on defined elimination criteria, such as studies on student emotions related to non-educational purposes. For systematic literature, the study has been divided into two research questions, i.e., (i) exploring key challenges of online learning and (ii) the role of sentiment analysis during online learning. These two questions have been answered based on arguments and evidence from the existing literature.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	2687-8860
ISBN:	9798350397758
Identifier:	10.1109/ISC257844.2023.10293422
Identifier:	10.1109/ISC257844.2023.10293422
Subjects:	Computing > Innovation and user experience
Depositing User:	Marc Forster
Date Deposited:	09 Dec 2024 10:00
Last Modified:	09 Dec 2024 10:00
URI:	https://repository.uwl.ac.uk/id/eprint/12977

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