Although there is much discussion exploring the potential causes of plagiarism, there is limited research available which provides evidence as to the academic interventions which may help reduce this. This paper discusses a bespoke English for Academic Purposes programme introduced at the university level, aimed at improving the academic writing standards of students, reducing plagiarism, and detecting cases of contract cheating. Results from 12 semesters of academic misconduct data demonstrate a 37.01% reduction in instances of detected plagiarism following the intervention, (...) but due to limited data, cannot demonstrate a direct impact on reducing detected rates of contract cheating. The results also show a lower than expected proportion of plagiarised assignments among submissions. (shrink)

Despite a continued focus exploring the factors related to plagiarism, the relationship between English language ability and plagiarism occurrences is not fully understood. Multiple studies involving student or faculty self-reporting of plagiarism have shown that students often claim English language ability is one of the main reasons why they commit plagiarism offences; however, little research has tested these claims in a rigorous, quantitative manner. This paper presents the findings of an analysis of data collected in a private, international university located (...) in Vietnam, from non-native English speaking students studying business degrees. Analysis of the data builds on previous studies by showing that there are statistically significant differences in the English language abilities of students who have previously committed plagiarism offences, compared to students who have not, suggesting that programmes designed to improve the academic English skills of non-native English speaking students may help reduce incidences of plagiarism. (shrink)

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial (...) big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)