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.     

Open publishing

There is increasing pressure from academia to make the results of publicly funded research freely available to all. Preprint archives have grown up in some disciplines, and institutional archives are now being developed. While openly accessible preprints seem to have damaged publishers less than was originally feared, the final published versions of articles – when combined with sophisticated retrieval software – may turn out to be a different matter. Publishers have already done much to make their content more accessible through bundling, consortia licensing, access for less‐developed countries, more liberal customer and author agreements, and collective licensing. Some are experimenting with open access journals, although this may not work in all disciplines. It seems clear, however, that there are elements of value in journals themselves, and in the functions performed by journal publishers, which should survive.     

Evaluating the impact of an academic teacher development program: practical realities of an evidence-based study

This study aimed to assess the impact of an entire academic teacher development programme at a Midwestern masters comprehensive university in the United States over a period of five years by examining changes in teaching and student outcomes of nine randomly selected programme participants. Researchers analysed syllabi, course evaluations, grade distributions, programme activity reports, interviews and vita, and concluded that the centre’s impact was greatest on tenure-track faculty who struggled with their teaching. Far more useful to future improvement, however, were the insights gained from reflection on study methods, the researchers’ emotional responses to their findings, their struggle to make sense of the data and grappling with the definition of ‘impact’. In this paper, the researchers share useful lessons learned and illustrate the difficulties inherent in evaluating academic development programmes, with a view to qualifying recommendations currently prevailing in the literature.