Cheating among elementary school children: A machine learning approach

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean accuracy of 81.43%. Cheating was most strongly predicted by children's beliefs about the acceptability of cheating and the observed prevalence and frequency of peer cheating at school. These findings provide important insights about the early development of academic cheating, and how to promote academic integrity and limit cheating before it becomes entrenched. The present research demonstrates that machine learning can be effectively used to analyze developmental data.     

International perceptions across the sea: Patterns in Japanese young adults

The international perceptions of Japanese young adults were explored in 172 professional education students in two Tokyo colleges. They classified 30 nations according to their “current” and “future” affinity to Japan. Little difference was detected in the Q-sorts between institutions arid sexes, while the “current” and “future” sorts correlated .96 with each other. In cluster analyses, many two-nation units (e.g., Czechoslovakia-Yugoslavia) were found to be the building blocks for the overall pattern. The total configuration consisted of three broad groups: mostly Western European nations, problematical nations (China, Russia, Israel, Egypt, etc.), and remote and neutral nations. Structurally, the results were similar to those for American young adults, but the contents were different. The notion of cultural metastereotypes thus received tentative support.     

Can a systematic assessment moderation process assure the quality and integrity of assessment practice while supporting the professional development of casual academics?

There has been a threefold increase in the employment of casual academics in Australian universities within the last 20 years, to the extent that most teaching and marking is now undertaken by casual academics, also known as sessional staff. Yet, casualised teaching and assessment has been considered a risk to student engagement and success, and casual academics report a lack of professional development and increased feelings of marginalisation within the academy. Concurrently, the quality assurance of teaching and assessment in higher education has become a central focus of the government-funded regulatory organisation, the Tertiary Education Quality and Standards Agency (TEQSA). Situated within this context, we report on an assessment moderation process that could support casual academics’ contextualised professional development, generate a sense of connectedness and collegiality and fulfil the requirements of TEQSA. Such processes may ensure that workforce growth in the higher education system supports a robust quality assurance and regulatory framework.