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.     

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.     

Emotional labour,burnout and job satisfaction in UK teachers: the role of workplace social support

Although teaching has been described as a profoundly emotional activity, little is known about the emotional demands faced by teachers or how this impacts on their well-being. This study examined relationships between ‘emotional labour’, burnout (emotional exhaustion, depersonalisation and personal accomplishment) and job satisfaction in a sample of UK teachers. Also examined was whether workplace social support moderated any relationships found between emotional labour and strain. The relationship between job experience and emotional labour was also investigated. Six hundred and twenty-eight teachers working in secondary schools in the UK completed questionnaires. Significant associations were observed between emotional labour and all outcomes, with a positive relationship found between emotional labour and personal accomplishment. Some evidence was found that social support mitigates the negative impact of emotional demands on emotional exhaustion, feelings of personal accomplishment and job satisfaction. More experienced teachers reported higher levels of emotional labour. Findings highlight the need for teacher-training programmes to raise awareness of the emotional demands of teaching and consider ways to enhance emotion regulation skills in experienced as well as recently qualified staff.