Purpose The purpose of this paper is to examine the impact of internet censorship, which is represented by the Great Fire Wall, on Chinese internet users’ self-censorship. Design/methodology/approach A 3×2 factorial experiment (n=315) is designed. Different patterns of censorship (soft censorship, compared censorship, and hard censorship) and the justification of internet regulation are involved in the experiment as two factors. The dependent variable is self-censorship which is measured through the willingness to speak about sensitive issues and the behavior of refusing to sign petitions with true names. Findings The results show that perceived internet censorship significantly decreases the willingness to talk about sensitive issues and the likelihood of signing petitions with true names. The justification of censorship significantly decreases self-censorship on the behaviors of petition signing. Although there are different patterns of internet censorship that Chinese netizens may encounter, they do not differ from each other in causing different levels of self-censorship. Research limitations/implications The subjects are college students who were born in the early 1990s, and the characteristics of this generation may influence the results of the experiment. The measurement of self-censorship could be refined. Originality/value The study contributes to the body of literature about internet regulation because it identifies a causal relationship between the government’s internet censorship system and ordinary people’s reaction to the regulation in an authoritarian regime. Unpacking different patterns of censorship and different dimensions of self-censorship depicts the complexity of censoring and being censored.
Background At the initial stage of COVID-19 outbreak, most medical education institutions in China had to accept the sudden shift from classroom teaching to nearly 100% online instruction for different curricula. However, little has been known about medical students’ learning efficiency when learning has been completely conducted online. This study aimed at investigating medical students’ perspectives on online learning efficiency during the early phase of the COVID-19 outbreak and finding possible factors that could damage online learning efficiency. Methods Between May and July, 2020, the authors electronically distributed a self-designed questionnaire to all the 780 medical students who attended the Rural-oriented Free Tuition Medical Education program in Guangxi Medical University that locates in the southwestern China. Data on participant demographics, learning phases, academic performance, and perceptions regarding learning efficiency of online and classroom learning were collected. Wilcoxon rank sum test, Kruskal Wallis test, and polynomial Logistic regression were employed to detect differences of learning efficiency between online and classroom learning, and associations among learning phases, academic performance and online learning efficiency. Results A total of 612 medical students validly responded to this survey (valid response rate 78.46%), and they reported more positive perceptions of efficiency in the circumstance of face-to-face learning than of online learning despite of gender (P<0.001), learning phases (P<0.01), and academic performance (P<0.01). Learning phases and academic performance positively corelated with online learning efficiency (P<0.01). In responders’ opinion, the five top factors that most damaged online learning efficiency were low academic motivation, poor course design, inferiority in online teaching ability, limited interactions between faculty and students or among students, and insufficient learner engagement. Conclusion This study indicates obviously negative impact brought by pure online learning on perceived learning efficiency of medical students, and positive associations amid learning phases, academic performance, and online learning efficiency. We advise that instead of pure online instruction, more effort should be put into developing new online course design to improve learning efficiency when online instruction is conducted in large scale, and learning phase and academic performance should be taken into account for effective implementation of online learning.
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