Zerong Xie

Purpose: This study examines how the sentiments and perceptions of Chinese students studying abroad evolved during and after the COVID-19 pandemic. Drawing on social media data, the study identifies key factors related to educational choice, vocational preparation and transitions into employment. Design/methodology/approach: This study analyzed nearly 95,000 Weibo posts related to studying abroad and COVID-19. Computational data mining methods, including sentiment analysis and topic modeling, were employed to identify key themes and emotional patterns among Chinese students considering studying abroad. The Granger causality test was utilized to explore the relationships between COVID-19 case numbers and sentiment scores. Findings: Results revealed that policy factors, health considerations and future development concerns related to degree recognition, graduate employability, salary expectations, job stability and career competitiveness are critical issues affecting Chinese students’ decisions to study abroad. These concerns persist beyond the immediate impacts of the pandemic. Originality/value: By applying social media analytics to real-time public discourse, this study offers a data-driven approach to understanding the evolving concerns of Chinese students in the context of international education during and after a global pandemic. The findings provide evidence for educational institutions, policymakers and other stakeholders seeking to develop responsive strategies and strengthen support services for Chinese students studying abroad.

STEAM teacher education increasingly asks future teachers to support students’ creativity and entrepreneurial mindset through interdisciplinary problem solving (Chiu & Hwang, 2026; Chiu et al., 202...

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development.

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP location disclosure, categorized the topics using LDA topic modeling within the frameworks of communication privacy management, the networked public sphere, and digital democracy, and conducted sentiment analysis. We constructed separate semantic networks for positive and negative terms to examine co-occurrence patterns. The results show that Weibo users are generally negative about this policy, as IP location may reveal personally identifiable information about individuals involved in discussions of online social/political events. Mandatory transparency, while intended to enhance accountability, functions as a mandatory visibility regime that reshapes privacy boundaries and undermines inclusive deliberation. The findings contribute to the exploration of the impact of government-mandatory information privacy disclosure policies on the implementation of platform functionality, as well as changes in user sentiment, information behavior, and components of social media discourse.

In this paper, we present Synergy, a language model that bridges different levels of abstraction in an end-to-end fashion through a learned routing mechanism. Focusing on low-level linguistic abstraction, we trained our model as a byte-level language model. Our model spontaneously learns to tokenize bytes, producing fewer concept tokens than Byte-level Byte Pair Encoder (BBPE) tokenizers while keeping comparable performance. By comparing with Llama3, we observed an advantage of Synergy under the same model scale and training dataset size. Further studies show that the middle part (the higher abstraction part) of our model performs better when positional encodings are removed, suggesting the emergence of position-independent concepts. These findings demonstrate the feasibility of tokenizer-free architectures, paving the way for more robust and flexible pipelines.

With the evolution of China's market economy, the securities market is increasingly anchoring a pivotal role in the nation's economic landscape. Consequently, stock trend forecasting has garnered heightened attention among scholars and practitioners. This research pioneers the use of multimodal information to predict stock market fluctuations. Based on our experimental results, LSTM + Transformer performs better in handling multimodal data for stock movement prediction tasks regarding accuracy, F1-score, precision, and recall. Additionally, we employed the Granger causality test and Impulse response test to investigate the causal relationships between sentiment and stock trends, as well as the interplay between COVID-related indicators and stock trajectories. We identified discernible causal links between sentiments, COVID indicators, and stock trends for select pharmaceutical stocks. Our findings can provide valuable guidance for investors and market regulators, especially within the pharmaceutical industry. Understanding investor sentiment and the impact of the pandemic on severity can assist in effective stock commentary management and improve investment strategies.

This research studies the motivational factors used in educational video games through the lens of 6 C’s learning motivation model with text mining of the players’ reviews and comments. This research seeks to offer insight for game producers and educational institutions to investigate the effectiveness of these motivators for increasing player motivations and thus improving the quality of learning. Sentiment analysis and LDA topic modeling were used to analyze reviews of five selected video games on the Steam platform. The 6 C’s Learning Motivation Model guided text mining to analyze the motivational factors used in the games and how they contribute to user learning. The effectiveness of these motivational factors was discussed in conjunction with categorized text mining. Results show that the major motivation factors of educational games are ‘construction meaning’, ‘challenge’, and ‘control’ in the 6 C’s learning motivation model. Among them, users focus on whether the game’s content meets their interests and the construction of the educational meaning of the game. The advantage of control, a high degree of motivational factor in video games, may turn out to be a factor that leads to user churning when the game is not interesting or attractive enough. Previous educational game research seldom involved a large sample size for generalizable findings. In addition, this research extends the application of the 6 C’s learning motivation model to the digital educational gaming arena, providing a novel player-centric perspective. Based on the results, we provide recommendations and design considerations for educational game developers to enhance players’ experience and motivations.

This study analyzes 15,453 math topic-based Scratch projects of the online Scratch community to find the relations among K–12 math topics, usage of 13 programming elements, and project popularity. Among the six math topics, only statistics and calculus significantly contribute to projecting popularity. In addition, noncore programming elements, including sound and look, are widely adapted across various math topics and contribute significantly to project popularity. Although some studies discussed the integration of computation thinking (CT) and math in the classrooms, scant research focuses on the effect of math topics on CT element adoption for achieving better integration in the creative learning context. Moreover, few studies involve a large sample size for generalizable findings.

The world is witnessing an unpredictable COVID-19 pandemic that has impacted all levels of online education, shaping future trends. However, this shift was so sudden and drastic that unrevealed puzzles exist regarding the public’s authentic opinion towards online classes, even though three years have passed. Many experts and policymakers have conducted qualitative and quantitative research to explore effective pedagogies, the satisfaction of different stakeholders, and factors influential on learners’ performance. However, scant studies have examined personal opinions and concerns toward online classes hidden behind people’s anonymous posts on social media. This research investigates the sentiments, concerns, and their variance with time regarding online classes by learners and educators on Reddit, which is a dominant social network among them. Data were collected via the official API from identified relevant subreddits and keyword search results across Reddit. Sentiment analysis was applied to reveal their emotions and their changes. Topic modeling was conducted to discover the concerns hidden in the posts. The results revealed the concerns about online classes, such as severe cheating behaviors, and showed doubts about previous strategies to solve disadvantages in online classes. In addition, the results verified the habitual difference and motivations of social media usage between educators and learners.