In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

Advancements in online learning have created new opportunities for students to enhance academic progression and broaden educational accessibility. However, notable concerns have arisen regarding the reliability of Facial Emotion Recognition (FER) in e-Learning. The variability in facial expressions, such as micro-expressions and rapid transitions between emotions, poses a challenge for consistent emotion classification from single-frame images, with state-of-the-art models recording accuracy scores between 64% and 80% on the FER-2103 dataset. To mitigate these concerns, we curated a foundational Base dataset comprised of the emotions encountered in e-Learning contexts (Bored, Confused, Engaged, Frustrated, and Neutral). The Base dataset was then supplemented with video data recorded from 100 students, 50 from the United Kingdom (UK) and 50 from Saudi Arabia, participating in 10 e-Learning sessions. The sessions were segmented into single-frame images and annotated by class. We then partitioned the supplemented Base dataset into two datasets, representing the UK and Saudi Arabia, respectively. To address the inherent limitations observed in single-frame FER, this study proposes a Multi-Frame Transfer Learning (MFTL) framework aimed at improving emotion classification in e-Learning contexts. The proposed framework combines an augmented MobileNet-V1 architecture with a custom temporal algorithm to classify the Dominant Emotion (DE) from a fixed-length sequence of frames. Experimental results on single-frame images from datasets 1 and 2 recorded accuracy, f1-score, and Matthews Correlation Coefficient (MCC) scores of 0.8724, 0.8726, 0.8407, and 0.8998, 0.8991, 0.8752, respectively. We then conducted 15 experiments using representative subsets of sequential video excerpts, with a total duration of 30 minutes. The proposed framework’s multi-frame approach recorded an average accuracy calculated across both datasets of 0.8778, with datasets 1 and 2 achieving 0.8646 and 0.8929, respectively. These findings validate our approach, evidenced by high classification scores and the framework’s efficacy in handling micro-expressions and emotional outliers. In conclusion, the proposed framework represents a substantial advancement in FER, offering a robust and refined approach tailored for online education. As e-Learning becomes increasingly integral to education, the insights and methodologies presented here propose to enhance student engagement, learning outcomes, and the overall online educational experience.

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.