Tracking public opinion about online education over COVID-19 in China

Zhou, Mingming; Mou, Hao

doi:10.1007/s11423-022-10080-5

Cited by 12 publications

(8 citation statements)

References 81 publications

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“…The survey confirmed that the online learning effectiveness was moderate (Mean = 3.56), echoing the findings of a study by scholars Zhou and Mou (2022) who found that positive attitudes toward online education has been steadily declining in China. According to the demographic profile, approximately one-third of the students may not graduate with a diploma certificate, which is consistent with the low online learning effectiveness.…”

Section: Discussionsupporting

confidence: 83%

Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China

Wider²,

Wong³

et al. 2023

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Due to the Zero-COVID policies implemented in China, students have accepted online education for curriculum learning for a considerable period of time. The objective of the current study was to determine the relationship between perceived usefulness, perceived ease of use, gender, and the online learning effectiveness of emerging adult learners in Guangzhou, China, who were enrolled in a continuing education program. The research framework was created in accordance with the Technology Acceptance Model (TAM). The convenient sampling and snowball sampling methods were used to collect 123 respondents for the study. Findings revealed that perceived usefulness and perceived ease of use were positively associated with online learning effectiveness, but gender was not. The study recommended that school administrators enhance students’ perceived usefulness and perceived ease of use to improve the online learning effectiveness. The findings of this study will provide scholars with insights for improving the investment effectiveness of adult education programs. In addition, because online learning tends to be more adaptable and economical than traditional classes, the increase in learning effectiveness can provide schools with justifications to continue utilizing online education even after the pandemic.

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Section: Discussionsupporting

confidence: 83%

Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China

Wider²,

Wong³

et al. 2023

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“…Exploratory research [48] into online education sentiment during the COVID-19 pandemic in China, leveraging Sina Weibo microblogs, offered insights into public perceptions across different pandemic phases. Additional studies [49][50][51][52] applied sentiment analysis to evaluate online course feedback and student reviews in higher education, highlighting the potential of sentiment analysis in enhancing educational quality.…”

Section: Related Workmentioning

confidence: 99%

Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

Louati,

Albanyan

et al. 2024

Computers

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Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces.

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“…The compilation of related work in sentiment analysis within the context of education reflects a trajectory of research focused on understanding and enhancing student experiences. Zhou et al [17] conducted a study analyzing public sentiment towards online education in China through Sina Weibo microblogs during the pre-pandemic, amid-pandemic, and post-pandemic phases. This research provided insights into the evolution of public opinion on online learning, particularly during the critical times of the COVID-19 outbreak, highlighting the shift towards digital platforms in education.…”

Section: Sentimental Analysis In Educationmentioning

confidence: 99%

Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

Louati,

Kariri

et al. 2023

Applied Sciences

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This study presents the development of a sentimental analysis system for high education students using Arabic text. There is a gap in the literature concerning understanding the perceptions and opinions of students in Saudi Arabia Universities regarding their education beyond COVID-19. The proposed SVM Sentimental Analysis for Arabic Students’ Course Reviews (SVM-SAA-SCR) algorithm is a general framework that involves collecting student reviews, preprocessing them, and using a machine learning model to classify them as positive, negative, or neutral. The suggested technique for preprocessing and classifying reviews includes steps such as collecting data, removing irrelevant information, tokenizing, removing stop words, stemming or lemmatization, and using pre-trained sentiment analysis models. The classifier is trained using the SVM algorithm and performance is evaluated using metrics such as accuracy, precision, and recall. Fine-tuning is done by adjusting parameters such as kernel type and regularization strength to optimize performance. A real dataset provided by the deanship of quality at Prince Sattam bin Abdulaziz University (PSAU) is used and contains students’ opinions on various aspects of their education. We also compared our algorithm with CAMeLBERT, a state-of-the-art Dialectal Arabic model. Our findings show that while the CAMeLBERT model classified 70.48% of the reviews as positive, our algorithm classified 69.62% as positive which proves the efficiency of the suggested SVM-SAA-SCR. The results of the proposed model provide valuable insights into the challenges and obstacles faced by Arab Universities post-COVID-19 and can help to improve their educational experience.

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Tracking public opinion about online education over COVID-19 in China

Cited by 12 publications

References 81 publications

Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China

Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China

Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

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